Meeting the Challenge of COVID-19 in DHQ Orthopaedic Department
Bibliographic record
Abstract
Introduction: The World Health Organization (WHO) declared Covid-19 as a pandemic on March 11, 2020. Not only that the COVID-19 pandemic has brought the world to a complete lockdown but also burdened healthcare systems across the world immensely. Objective: In this paper, we discuss the different strategies we adopted in the Orthopaedics Department of District Head Quarter [DHQ] hospital Rawalpindi, during this ongoing pandemic and share our experience of successfully but cautiously providing orthopedic services to patients in a public hospital. We compare our workload and output of May 2020 [pandemic phase] to May 2019 [standard/normal phase]. Methodology: The Hospital policy was changed after the COVID-19 pandemic. We increased public awareness and reduced load in the OPD using different strategies. We postponed all elective cases; focusing our logistics and resources only on the patients in urgent need of surgical management. A minimum number of doctors and OTAs were allocated on each list. Inwards, the patient stay was reduced. As a standard PCR test for COVID-19 was expensive, we devised our screening through history, examination, and routine investigations. Results: The average stay inwards was reduced from 6.4±4.6 days in May 2019 to 2.7±3.6 days in May 2020. The decrease in the stay was statistically significant (p=.0206) and was associated with a 24.4% increase in the number of total patient admissions in May 2020 (n=56) as compared to May 2019 (n=45). The number of surgeries performed month to month was very similar in normal and pandemic periods. Our OPD patient attendance dropped from 200-250 patients per day in 2019 to 60-70 during the ongoing pandemic phase. Conclusion: We believe that sharing experiences between health care actors allows us to develop an effective strategy to provide the very best care to our patients during the COVID-19 pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".