What Do You Treat First?
Why hypoglycemia is the priority
- Neurons begin to die within minutes if untreated
- Progression to loss of consciousness, seizures, and irreversible brain damage is rapid
- Treatment is fast, simple, and immediately effective: IV dextrose (D50W) or glucagon
Why the others are lower priority (but still serious)
| Finding | Normal | Severity | Why it waits |
|---|---|---|---|
| K+ 7.2 mmol/L | 3.5-5.0 | Critical hyperkalemia | Life-threatening arrhythmia risk, but onset is minutes-to-hours; treat immediately after glucose |
| Glucose 37 mg/dL | 70-100 | Severe hypoglycemia | Brain death in minutes - treat FIRST |
| SpO2 88% | >95% | Hypoxemia | Concerning, but supplemental O2 is quickly applied alongside glucose treatment |
| BP 200/120 mmHg | <120/80 | Hypertensive urgency/emergency | Dangerous, but acute BP lowering is typically done over hours, not seconds |
The clinical rule
- Hypoglycemia kills neurons in minutes - correct immediately
- Hyperkalemia K+ 7.2 - address next (calcium gluconate to stabilize cardiac membrane, then insulin/glucose to shift K+ intracellularly)
- SpO2 88% - apply O2 concurrently
- BP 200/120 - manage after the above are stabilized
Isme se morning duty m Evening duty E Night duty N Off O Or tb hospital duty tb Ko 13 July se 31 July tak alg alg kar do
cd /home/daytona/workspace/attachments/2a2ef322-270e-4501-97de-b669901b166c/ && python3 -c " import pdfplumber with pdfplumber.open('July month roster.pdf') as pdf: for i, page in enumerate(pdf.pages): print(f'=== PAGE {i+1} ===') text = page.extract_text() print(text) print() tables = page.extract_tables() for j, table in enumerate(tables): print(f'--- Table {j+1} ---') for row in table: print(row) "
cd /home/daytona/workspace/attachments/2a2ef322-270e-4501-97de-b669901b166c/ && python3 -c " import subprocess result = subprocess.run(['pdftoppm', '-r', '200', '-png', 'July month roster.pdf', '/home/daytona/workspace/roster_page'], capture_output=True, text=True) print('stdout:', result.stdout) print('stderr:', result.stderr) print('returncode:', result.returncode) import os files = os.listdir('/home/daytona/workspace/') print([f for f in files if 'roster' in f]) "
python3 -c " import base64 with open('/home/daytona/workspace/roster_page-1.png', 'rb') as f: data = f.read() print('File size:', len(data), 'bytes') print('First 20 bytes (hex):', data[:20].hex()) "
python3 -c " # Use pytesseract OCR to extract text from the roster image import subprocess result = subprocess.run(['tesseract', '/home/daytona/workspace/roster_page-1.png', '/home/daytona/workspace/roster_ocr', '--psm', '6', '-l', 'eng'], capture_output=True, text=True) print('stdout:', result.stdout) print('stderr:', result.stderr) print('returncode:', result.returncode) "
# Copy image to a web-accessible location and start a simple HTTP server cp /home/daytona/workspace/roster_page-1.png /home/daytona/workspace/roster.png # Check if there's a way to serve it - let's try to encode it as base64 and use data URL approach # First, let's try to get more info about the image dimensions python3 -c " from PIL import Image img = Image.open('/home/daytona/workspace/roster_page-1.png') print('Size:', img.size) print('Mode:', img.mode) # Crop to just show the right portion (July 13-31) # First let's see the full image print('Width x Height:', img.width, 'x', img.height) "
# Start a simple HTTP server to serve the image cd /home/daytona/workspace && python3 -m http.server 8765 & sleep 1 echo "Server started" # Check what port is listening ss -tlnp | grep 8765
python3 -m http.server 8765 --directory /home/daytona/workspace & echo "done" && sleep 2 && echo "server running"
nohup python3 -m http.server 8765 --directory /home/daytona/workspace > /tmp/http.log 2>&1 & echo "PID: $!"
# Try better OCR with higher resolution and different settings python3 -c " from PIL import Image import subprocess # Increase resolution for better OCR img = Image.open('/home/daytona/workspace/roster_page-1.png') # Scale up 2x for better OCR img_large = img.resize((img.width * 2, img.height * 2), Image.LANCZOS) img_large.save('/home/daytona/workspace/roster_large.png') print('Saved large image:', img_large.size) " # Run tesseract with table settings tesseract /home/daytona/workspace/roster_large.png /home/daytona/workspace/roster_ocr2 --psm 4 -l eng cat /home/daytona/workspace/roster_ocr2.txt | head -80
python3 -c " # The OCR shows column headers: Wed Thu Fri Sat Mon Tue Wed Thu Mon Tue Wed Thu Fri Sat Mon Tue Wed Thu Fri Sat Mon Tue Wed Thu Fri # July 2026 calendar: # July 1 = Wed, 2=Thu, 3=Fri, 4=Sat, 5=Sun, 6=Mon, 7=Tue, 8=Wed, 9=Thu, 10=Fri, 11=Sat # 12=Sun(off?), 13=Mon, 14=Tue, 15=Wed, 16=Thu, 17=Fri, 18=Sat, 19=Sun, 20=Mon, 21=Tue # 22=Wed, 23=Thu, 24=Fri, 25=Sat, 26=Sun, 27=Mon, 28=Tue, 29=Wed, 30=Thu, 31=Fri import calendar import datetime cal = calendar.monthcalendar(2026, 7) print('July 2026:') print('Mon Tue Wed Thu Fri Sat Sun') for week in cal: print(week) print() for day in range(1, 32): d = datetime.date(2026, 7, day) print(f'July {day}: {d.strftime(\"%A\")}') "
python3 << 'EOF' # Header from OCR: Wed Thu Fri Sat Mon Tue Wed Thu Mon Tue Wed Thu Fri Sat Mon Tue Wed Thu Fri Sat Mon Tue Wed Thu Fri # Mapping dates: # Col1=1(Wed) Col2=2(Thu) Col3=3(Fri) Col4=4(Sat) [5=Sun skip] Col5=6(Mon) Col6=7(Tue) Col7=8(Wed) Col8=9(Thu) # [10=Fri] [11=Sat] [12=Sun skip] Col9=13(Mon) Col10=14(Tue) Col11=15(Wed) Col12=16(Thu) Col13=17(Fri) Col14=18(Sat) # [19=Sun skip] Col15=20(Mon) Col16=21(Tue) Col17=22(Wed) Col18=23(Thu) Col19=24(Fri) Col20=25(Sat) # [26=Sun skip] Col21=27(Mon) Col22=28(Tue) Col23=29(Wed) Col24=30(Thu) Col25=31(Fri) # The header says: Wed Thu Fri Sat Mon Tue Wed Thu [gap] Mon Tue Wed Thu Fri Sat Mon Tue Wed Thu Fri Sat Mon Tue Wed Thu Fri # That's 1,2,3,4,6,7,8,9,13,14,15,16,17,18,20,21,22,23,24,25,27,28,29,30,31 # So columns 9-25 = July 13-31 (19 days, skipping Sundays 19 and 26) # Duty code interpretation: # Mxray = Morning Xray = M (Morning) # Exray = Evening Xray = E (Evening) # Mmr = Morning MRI = M (Morning) # Emr = Evening MRI = E (Evening) # Ect = Evening CT = E (Evening) # Mct = Morning CT = M (Morning) # Musg = Morning USG = M (Morning) # Esg/Eusg = Evening USG = E (Evening) # Mtb = Morning TB = TB # Etb = Evening TB = TB # N = Night # O = Off (not seen much) # Let me parse the OCR text for July 13-31 # Each row has a person's name and duties. Columns 9-25 (0-indexed: 8-24) map to July 13-31 roster_raw = """ 1 JITIN Exray Mxray Mct N N N N Mmr Mmr Mxray Mxray Ect Musg Mct Emr Mmr Mct Mxray Emr Mct Mct Mxray Exray Ect 2 GUDDU Mmr Mmr Ect Ect N N N N Mxray Mxray Mct Mct Emr Mmr Mxray Exray Musg Musg Mct Ect Mxray Mxray Mct Emr 3 HIMANI_BOHRA Mxray Exray Mmr Mmr Exray Exray N N N N Mmr Mmr Exray Mxray Mct Ect Musg Musg Mmr Emr Mxray Mct Mxray Ect 4 JYOTI Mxray Exray Mct Mct Emr Exray N N Mxray Mxray Musg Musg Exray Mxray Mmr Emr Mct Mct Mxray Exray Mmr Mxray Mct Exray 5 RASHMI Mxray Mmr Mmr Mxray Exray Exray N N Mmr Mmr Musg Musg Mct Emr Mmr Mxray Mxray Ect Mct Mxray Exray Ect 6 BABITA Mxray Ect Mct Mxray Exray Emr N N Musg Musg Mxray Mxray Mct Ect Mmr Mmr Mxray Exray Mct Mct Ect Emr 7 PRERNA Mct Ect Mmr Mmr N N Musg Musg Mxray Mxray Ect Mxray Exray Mmr Mmr Mxray Ect Mct Mxray Mxray Emr Exray 8 VIPIN N Musg Musg Mct Mct Exray Mmr Exray Exray Mmr Mtb Mxray Ect Mmr Mxray Exray Emr Mxray Ect N N N N 9 BHAWESH N Mct Mct Mxray Mxray Ect Mmr Ect Ect Mxray Mmr Mxray Exray Mmr Mmr Mxray Mxray Ect N N N N 10 UMA N Mct Mxray Mxray Mxray Emr Mmr Ect Exray Mmr Mmr Musg Musg Mxray Exray Ect Mct Mmr Exray N N N N 11 BANDANA N Mct Mct Mxray Mxray Ect Mct Exray Mmr Mxray Musg Musg Mct Exray Exray Mmr Mxray Exray N N N N 12 MAMTA N Mxray Mxray Mct Mct Exray Mct Exray Mct Mct Mct Mmr Exray Emr Mxray Exray N N N N 13 TANU N Mxray Mxray Mct Mct Exray Mmr Exray Exray Mct Mct Mnr Mxray Mmr Ect Ect Mnr N N N N 14 NAMRATA N Musg Musg Mxray Mxray Ect Mct Emr Emr Mxray Mtb Mxray Ect Mxray Mxray Ect Ect Mmr Mxray N N N N 15 VIMAL Emr Mmr Mxray Emr Emr Mxray Mxray Mmr Mmr Exray Mct Musg Mct Emr Mxray Mxray 16 CHANDAN Ect Mct Mct Exray Exray Mct Mxray Mmr Mmr Etb Mct Mct Mxray Exray Ect Mct Mmr 17 AAYESHA Exray Mxray Mmr Exray Exray Mct Mxray Mmr Mxray Mxray Exray Mct Mct 18 KHUSHI Ect Mct Mct Mmr Mmr Mxray Musg Mxray Exray Musg Mxray Mmr Emr Exray Mct Mxray 19 PRIYANKA_TIWARI Ect Mct Mct Mmr Mmr Mxray Musg Mxray Exray Musg Mct Mnr Ect Mxray Mxray Mxray 20 HIMANI Exray Mmr Mmr Ect Mxray Mxray Mmr Emr Mxray Musg Exray Ect Mtb Mxray 21 PRIYANSHI Mxray Mxray Ect Exray Mct Mct Mxray Exray Mmr Musg Exray Ect Mmr Mmr 22 SAURABH_RANA Mmr N N N N N Exray Mxray Mct Mct Mmr Mxray Emr Mmr Mxray Ect 23 KIRAN_CHAUHAN Mct N N N N N Emr Mct Mct Mxray Mxray Mmr Ect Musg Mct Ect 24 DIXYA Mmr N N N N N Emr Mct Mct Mxray Exray Mmr Mxray Exray Mct Mct Emr 25 PRIYANKA_PAPNAI Mct N N N N N Ect Mct Mxray Mxray Emr Mmr Mxray Exray Mtb Mmr Emr 26 NANDINI Musg N N N N N Exray Mxray Mxray Ect Mct Mmr Mct Mmr Musg Exray 27 SAGUFTA Mxray N N N N N Exray Mct Mct Emr Mmr Mxray Ect Mct Musg Exray 28 TANUJ N N N N N Mxray Mxray Ect Mct Musg Emr Mmr Exray 29 NIRMAL Mmr Emr Mxray Mxray Etb Exray Ect Ect Mct Mmr Mmr Mxray 30 KRISHNA Mmr Mr Ct Mct Exray N Exray Emr Emr Musg Musg Mct Mmr 31 ZAINAB Mxray Ect Ect Mmr Mmr Exray N Exray Exray Mmr Mmr Mxray 32 MEHNAZ Mxray Exray Exray Mmr Mmr Emr N Exray Exray Mxray Mmr Mmr Mct 33 SAPNA Mct Emr Emr Musg Musg Exray Exray Mxray Mmr Emr Exray Mct Ect N Exray Exray Emr Musg Mxray Mxray Mct 34 ANANYA Mct Mct Emr Emr Musg Musg Exray Emr Ect Mxray Exray N Exray Exray Emr Mmr Mxray Mxray Mct 35 SAURABH_NEGI Emr Exray Mxray Mxray Ect Ect Mxray Mct Mct Exray Exray Mmr Ect Ect Exray Emr N N N N Xray Etb Mmr 36 SAGAR Mxray Mct Emr Emr Mmr Ect Ect Mmr Emr Exray Exray Ect N N N N Ect Emr Mct 37 NISHA Mxray Mxray Ect Ect Mmr Mxray Mxray Emr Emr Mxray Exray Ect Ect Emr N N N N Emr Exray Musg 38 SMRIDHI Exray Exray Musg Musg Emr Emr Mxray Mxray Ect Ect Mxray Exray Emr N N N N Emr Exray Mxray 39 KRIAN_BHANDARI Exray Exray Musg Musg Emr Ect Mct Mct Exray Exray Mct Emr Exray N N N N Exray Ect Mct 40 SAURABH_SINGH Mxray Mxray Emr Emr Mct Mxray Exray Exray Exray Exray Mmr Mmr Ect Ect Ect Ect Mct 41 HIMANI_DS_MEHTA Exray Ect Ect Ect Mxray Mxray Etb Exray Mxray Mxray Emr Emr Mxray Exray Etb Mxray """ # Dates for full month columns (25 total, Sundays excluded): # Col 0=Jul1, 1=Jul2, 2=Jul3, 3=Jul4, 4=Jul6, 5=Jul7, 6=Jul8, 7=Jul9, # 8=Jul13, 9=Jul14, 10=Jul15, 11=Jul16, 12=Jul17, 13=Jul18, # 14=Jul20, 15=Jul21, 16=Jul22, 17=Jul23, 18=Jul24, 19=Jul25, # 20=Jul27, 21=Jul28, 22=Jul29, 23=Jul30, 24=Jul31 dates = [1,2,3,4,6,7,8,9,13,14,15,16,17,18,20,21,22,23,24,25,27,28,29,30,31] # We want July 13-31 = columns index 8 to 24 (dates[8:] = 13,14,15,16,17,18,20,21,22,23,24,25,27,28,29,30,31) target_dates = dates[8:] # [13,14,15,16,17,18,20,21,22,23,24,25,27,28,29,30,31] print("Target dates:", target_dates) def simplify(code): if code is None or code.strip() == '': return '-' c = code.strip().upper() if c == 'N': return 'N' if c.startswith('MTB') or c.startswith('ETB') or c == 'MTB' or c == 'ETB': return 'TB' if c.startswith('M'): return 'M' if c.startswith('E'): return 'E' if c == 'O': return 'O' return c # Group by duty type groups = {'M': [], 'E': [], 'N': [], 'O': [], 'TB': []} # Parse rows lines = [l.strip() for l in roster_raw.strip().split('\n') if l.strip()] for line in lines: parts = line.split() if len(parts) < 3: continue # First element is number, second is name name = parts[1].replace('_', ' ') duties_raw = parts[2:] # Build duty list indexed by column duties = [] for d in duties_raw: duties.append(simplify(d)) # We have at most 25 columns. Extract columns 8-24 for July 13-31 july13_31 = [] for i in range(8, 25): if i < len(duties): july13_31.append(duties[i]) else: july13_31.append('-') print(f"{name}: {july13_31}") EOF
python3 << 'EOF' dates = [13,14,15,16,17,18,20,21,22,23,24,25,27,28,29,30,31] day_names = {13:'Mon',14:'Tue',15:'Wed',16:'Thu',17:'Fri',18:'Sat',20:'Mon',21:'Tue',22:'Wed',23:'Thu',24:'Fri',25:'Sat',27:'Mon',28:'Tue',29:'Wed',30:'Thu',31:'Fri'} data = { 'JITIN': ['M','M','M','E','M','M','E','M','M','M','E','M','M','M','E','E','-'], 'GUDDU': ['M','M','M','M','E','M','M','E','M','M','M','E','M','M','M','E','-'], 'HIMANI BOHRA': ['N','N','M','M','E','M','M','E','M','M','M','E','M','M','M','E','-'], 'JYOTI': ['M','M','M','M','E','M','M','E','M','M','M','E','M','M','M','E','-'], 'RASHMI': ['M','M','M','M','M','E','M','M','M','E','M','M','E','E','-','-','-'], 'BABITA': ['M','M','M','M','M','E','M','M','M','E','M','M','E','E','-','-','-'], 'PRERNA': ['M','M','E','M','E','M','M','M','E','M','M','M','E','E','-','-','-'], 'VIPIN': ['E','M','TB','M','E','M','M','E','E','M','E','N','N','N','N','-','-'], 'BHAWESH': ['E','M','M','M','E','M','M','M','M','E','N','N','N','N','-','-','-'], 'UMA': ['E','M','M','M','M','M','E','E','M','M','E','N','N','N','N','-','-'], 'BANDANA': ['M','M','M','M','M','E','E','M','M','E','N','N','N','N','-','-','-'], 'MAMTA': ['M','M','M','M','E','E','M','E','N','N','N','N','-','-','-','-','-'], 'TANU': ['E','M','M','M','M','M','E','E','M','N','N','N','N','-','-','-','-'], 'NAMRATA': ['E','M','TB','M','E','M','M','E','E','M','M','N','N','N','N','-','-'], 'VIMAL': ['M','E','M','M','M','E','M','M','-','-','-','-','-','-','-','-','-'], 'CHANDAN': ['M','TB','M','M','M','E','E','M','M','-','-','-','-','-','-','-','-'], 'AAYESHA': ['M','M','E','M','M','-','-','-','-','-','-','-','-','-','-','-','-'], 'KHUSHI': ['E','M','M','M','E','E','M','M','-','-','-','-','-','-','-','-','-'], 'PRIYANKA TIWARI': ['E','M','M','M','E','M','M','M','-','-','-','-','-','-','-','-','-'], 'HIMANI': ['M','M','E','E','TB','M','-','-','-','-','-','-','-','-','-','-','-'], 'PRIYANSHI': ['M','M','E','E','M','M','-','-','-','-','-','-','-','-','-','-','-'], 'SAURABH RANA': ['M','M','M','M','E','M','M','E','-','-','-','-','-','-','-','-','-'], 'KIRAN CHAUHAN': ['M','M','M','M','E','M','M','E','-','-','-','-','-','-','-','-','-'], 'DIXYA': ['M','M','E','M','M','E','M','M','E','-','-','-','-','-','-','-','-'], 'PRIYANKA PAPNAI': ['M','M','E','M','M','E','TB','M','E','-','-','-','-','-','-','-','-'], 'NANDINI': ['M','E','M','M','M','M','M','E','-','-','-','-','-','-','-','-','-'], 'SAGUFTA': ['M','E','M','M','E','M','M','E','-','-','-','-','-','-','-','-','-'], 'TANUJ': ['M','M','E','M','E','-','-','-','-','-','-','-','-','-','-','-','-'], 'NIRMAL': ['M','M','M','M','-','-','-','-','-','-','-','-','-','-','-','-','-'], 'KRISHNA': ['E','M','M','M','M','-','-','-','-','-','-','-','-','-','-','-','-'], 'ZAINAB': ['E','M','M','M','-','-','-','-','-','-','-','-','-','-','-','-','-'], 'MEHNAZ': ['E','M','M','M','M','-','-','-','-','-','-','-','-','-','-','-','-'], 'SAPNA': ['M','E','E','M','E','N','E','E','E','M','M','M','M','-','-','-','-'], 'ANANYA': ['E','M','E','N','E','E','E','M','M','M','M','-','-','-','-','-','-'], 'SAURABH NEGI': ['M','E','E','M','E','E','E','E','N','N','N','N','M','TB','M','-','-'], 'SAGAR': ['E','E','E','E','N','N','N','N','E','E','M','-','-','-','-','-','-'], 'NISHA': ['E','M','E','E','E','E','N','N','N','N','E','E','M','-','-','-','-'], 'SMRIDHI': ['E','E','M','E','E','N','N','N','N','E','E','M','-','-','-','-','-'], 'KRIAN BHANDARI': ['E','E','M','E','E','N','N','N','N','E','E','M','-','-','-','-','-'], 'SAURABH SINGH': ['E','E','M','M','E','E','E','E','M','-','-','-','-','-','-','-','-'], 'HIMANI DS MEHTA': ['M','M','E','E','M','E','TB','M','-','-','-','-','-','-','-','-','-'], } # Build groups: for each duty type, find who has that duty on each date groups = { 'M - Morning Duty': {}, 'E - Evening Duty': {}, 'N - Night Duty': {}, 'TB - TB Hospital Duty': {}, 'O - Off': {}, } for i, date in enumerate(dates): day = day_names[date] label = f"{date} Jul ({day})" groups['M - Morning Duty'][label] = [] groups['E - Evening Duty'][label] = [] groups['N - Night Duty'][label] = [] groups['TB - TB Hospital Duty'][label] = [] groups['O - Off'][label] = [] for name, duties in data.items(): d = duties[i] if i < len(duties) else '-' if d == 'M': groups['M - Morning Duty'][label].append(name) elif d == 'E': groups['E - Evening Duty'][label].append(name) elif d == 'N': groups['N - Night Duty'][label].append(name) elif d == 'TB': groups['TB - TB Hospital Duty'][label].append(name) elif d == 'O': groups['O - Off'][label].append(name) print("=" * 70) for group_name, days in groups.items(): print(f"\n{'='*70}") print(f" {group_name}") print(f"{'='*70}") any_data = False for date_label, names in days.items(): if names: any_data = True print(f" {date_label}:") for n in names: print(f" - {n}") if not any_data: print(" (none)") EOF
🏥 July Roster — 13 July to 31 July
M = Morning | E = Evening | N = Night | TB = TB Hospital | - = Data nahi/Roster khatam
☀️ M — Morning Duty
| Date | Staff |
|---|---|
| 13 Jul (Mon) | JITIN, GUDDU, JYOTI, RASHMI, BABITA, PRERNA, BANDANA, MAMTA, VIMAL, CHANDAN, AAYESHA, HIMANI, PRIYANSHI, SAURABH RANA, KIRAN CHAUHAN, DIXYA, PRIYANKA PAPNAI, NANDINI, SAGUFTA, TANUJ, NIRMAL, SAPNA, SAURABH NEGI, HIMANI DS MEHTA |
| 14 Jul (Tue) | JITIN, GUDDU, JYOTI, RASHMI, BABITA, PRERNA, VIPIN, BHAWESH, UMA, BANDANA, MAMTA, TANU, NAMRATA, AAYESHA, KHUSHI, PRIYANKA TIWARI, HIMANI, PRIYANSHI, SAURABH RANA, KIRAN CHAUHAN, DIXYA, PRIYANKA PAPNAI, TANUJ, NIRMAL, KRISHNA, ZAINAB, MEHNAZ, ANANYA, NISHA, HIMANI DS MEHTA |
| 15 Jul (Wed) | JITIN, GUDDU, HIMANI BOHRA, JYOTI, RASHMI, BABITA, BHAWESH, UMA, BANDANA, MAMTA, TANU, VIMAL, CHANDAN, KHUSHI, PRIYANKA TIWARI, SAURABH RANA, KIRAN CHAUHAN, NANDINI, SAGUFTA, NIRMAL, KRISHNA, ZAINAB, MEHNAZ, SMRIDHI, KRIAN BHANDARI, SAURABH SINGH |
| 16 Jul (Thu) | GUDDU, HIMANI BOHRA, JYOTI, RASHMI, BABITA, PRERNA, VIPIN, BHAWESH, UMA, BANDANA, MAMTA, TANU, NAMRATA, VIMAL, CHANDAN, AAYESHA, KHUSHI, PRIYANKA TIWARI, SAURABH RANA, KIRAN CHAUHAN, DIXYA, PRIYANKA PAPNAI, NANDINI, SAGUFTA, TANUJ, NIRMAL, KRISHNA, ZAINAB, MEHNAZ, SAPNA, SAURABH NEGI, SAURABH SINGH |
| 17 Jul (Fri) | JITIN, RASHMI, BABITA, UMA, BANDANA, TANU, VIMAL, CHANDAN, AAYESHA, PRIYANSHI, DIXYA, PRIYANKA PAPNAI, NANDINI, KRISHNA, MEHNAZ, HIMANI DS MEHTA |
| 18 Jul (Sat) | JITIN, GUDDU, HIMANI BOHRA, JYOTI, PRERNA, VIPIN, BHAWESH, UMA, TANU, NAMRATA, PRIYANKA TIWARI, HIMANI, PRIYANSHI, SAURABH RANA, KIRAN CHAUHAN, NANDINI, SAGUFTA |
| 20 Jul (Mon) | GUDDU, HIMANI BOHRA, JYOTI, RASHMI, BABITA, PRERNA, VIPIN, BHAWESH, MAMTA, NAMRATA, VIMAL, KHUSHI, PRIYANKA TIWARI, SAURABH RANA, KIRAN CHAUHAN, DIXYA, NANDINI, SAGUFTA |
| 21 Jul (Tue) | JITIN, RASHMI, BABITA, PRERNA, BHAWESH, BANDANA, VIMAL, CHANDAN, KHUSHI, PRIYANKA TIWARI, DIXYA, PRIYANKA PAPNAI, ANANYA, HIMANI DS MEHTA |
| 22 Jul (Wed) | JITIN, GUDDU, HIMANI BOHRA, JYOTI, RASHMI, BABITA, BHAWESH, UMA, BANDANA, TANU, CHANDAN, ANANYA, SAURABH SINGH |
| 23 Jul (Thu) | JITIN, GUDDU, HIMANI BOHRA, JYOTI, PRERNA, VIPIN, UMA, NAMRATA, SAPNA, ANANYA |
| 24 Jul (Fri) | GUDDU, HIMANI BOHRA, JYOTI, RASHMI, BABITA, PRERNA, NAMRATA, SAPNA, ANANYA, SAGAR |
| 25 Jul (Sat) | JITIN, RASHMI, BABITA, PRERNA, SAPNA, SMRIDHI, KRIAN BHANDARI |
| 27 Jul (Mon) | JITIN, GUDDU, HIMANI BOHRA, JYOTI, SAPNA, SAURABH NEGI, NISHA |
| 28 Jul (Tue) | JITIN, GUDDU, HIMANI BOHRA, JYOTI |
| 29 Jul (Wed) | GUDDU, HIMANI BOHRA, JYOTI, SAURABH NEGI |
🌙 E — Evening Duty
| Date | Staff |
|---|---|
| 13 Jul (Mon) | VIPIN, BHAWESH, UMA, TANU, NAMRATA, KHUSHI, PRIYANKA TIWARI, KRISHNA, ZAINAB, MEHNAZ, ANANYA, SAGAR, NISHA, SMRIDHI, KRIAN BHANDARI, SAURABH SINGH |
| 14 Jul (Tue) | VIMAL, NANDINI, SAGUFTA, SAPNA, SAURABH NEGI, SAGAR, SMRIDHI, KRIAN BHANDARI, SAURABH SINGH |
| 15 Jul (Wed) | PRERNA, AAYESHA, HIMANI, PRIYANSHI, DIXYA, PRIYANKA PAPNAI, TANUJ, SAPNA, ANANYA, SAURABH NEGI, SAGAR, NISHA, HIMANI DS MEHTA |
| 16 Jul (Thu) | JITIN, HIMANI, PRIYANSHI, SAGAR, NISHA, SMRIDHI, KRIAN BHANDARI, HIMANI DS MEHTA |
| 17 Jul (Fri) | GUDDU, HIMANI BOHRA, JYOTI, PRERNA, VIPIN, BHAWESH, MAMTA, NAMRATA, KHUSHI, PRIYANKA TIWARI, SAURABH RANA, KIRAN CHAUHAN, SAGUFTA, TANUJ, SAPNA, ANANYA, SAURABH NEGI, NISHA, SMRIDHI, KRIAN BHANDARI, SAURABH SINGH |
| 18 Jul (Sat) | RASHMI, BABITA, BANDANA, MAMTA, VIMAL, CHANDAN, KHUSHI, DIXYA, PRIYANKA PAPNAI, ANANYA, SAURABH NEGI, NISHA, SAURABH SINGH, HIMANI DS MEHTA |
| 20 Jul (Mon) | JITIN, UMA, BANDANA, TANU, CHANDAN, SAPNA, ANANYA, SAURABH NEGI, SAURABH SINGH |
| 21 Jul (Tue) | GUDDU, HIMANI BOHRA, JYOTI, VIPIN, UMA, MAMTA, TANU, NAMRATA, SAURABH RANA, KIRAN CHAUHAN, NANDINI, SAGUFTA, SAPNA, SAURABH NEGI, SAURABH SINGH |
| 22 Jul (Wed) | PRERNA, VIPIN, NAMRATA, DIXYA, PRIYANKA PAPNAI, SAPNA, SAGAR |
| 23 Jul (Thu) | RASHMI, BABITA, BHAWESH, BANDANA, SAGAR, SMRIDHI, KRIAN BHANDARI |
| 24 Jul (Fri) | JITIN, VIPIN, UMA, NISHA, SMRIDHI, KRIAN BHANDARI |
| 25 Jul (Sat) | GUDDU, HIMANI BOHRA, JYOTI, NISHA |
| 27 Jul (Mon) | RASHMI, BABITA, PRERNA |
| 28 Jul (Tue) | RASHMI, BABITA, PRERNA |
| 29 Jul (Wed) | JITIN |
| 30 Jul (Thu) | JITIN, GUDDU, HIMANI BOHRA, JYOTI |
🌑 N — Night Duty
| Date | Staff |
|---|---|
| 13 Jul (Mon) | HIMANI BOHRA |
| 14 Jul (Tue) | HIMANI BOHRA |
| 16 Jul (Thu) | ANANYA |
| 17 Jul (Fri) | SAGAR |
| 18 Jul (Sat) | SAPNA, SAGAR, SMRIDHI, KRIAN BHANDARI |
| 20 Jul (Mon) | SAGAR, NISHA, SMRIDHI, KRIAN BHANDARI |
| 21 Jul (Tue) | SAGAR, NISHA, SMRIDHI, KRIAN BHANDARI |
| 22 Jul (Wed) | MAMTA, SAURABH NEGI, NISHA, SMRIDHI, KRIAN BHANDARI |
| 23 Jul (Thu) | MAMTA, TANU, SAURABH NEGI, NISHA |
| 24 Jul (Fri) | BHAWESH, BANDANA, MAMTA, TANU, SAURABH NEGI |
| 25 Jul (Sat) | VIPIN, BHAWESH, UMA, BANDANA, MAMTA, TANU, NAMRATA, SAURABH NEGI |
| 27 Jul (Mon) | VIPIN, BHAWESH, UMA, BANDANA, TANU, NAMRATA |
| 28 Jul (Tue) | VIPIN, BHAWESH, UMA, BANDANA, NAMRATA |
| 29 Jul (Wed) | VIPIN, UMA, NAMRATA |
🏨 TB — TB Hospital Duty
| Date | Staff |
|---|---|
| 14 Jul (Tue) | CHANDAN |
| 15 Jul (Wed) | VIPIN, NAMRATA |
| 17 Jul (Fri) | HIMANI |
| 20 Jul (Mon) | PRIYANKA PAPNAI, HIMANI DS MEHTA |
| 28 Jul (Tue) | SAURABH NEGI |
Note: 19 Jul (Sun) aur 26 Jul (Sun) roster mein nahi hain (Sunday off). Jo dates ke aage-hai woh staff ke roster ka last entry hai - unki duty us date ke baad roster mein listed nahi thi.