Changing practices of delivering orthopedic and coronavirus disease 2019 care
Bibliographic record
Abstract
Purpose: Coronavirus disease 2019 (COVID-19) pandemic has caused severe disruption of services for other health-related ailments. This study was done to assess change in practices of orthopedic surgeons, availability of proper training and personal protective equipment (PPEs), and changes in hospital setup/preparedness for the management of COVID-19 patients. Materials and Methods: A pan-India online survey was done among practicing orthopedic surgeons. Orthopedicians not practicing in India, trainees, and those who had left their practice before the pandemic were excluded from the study. Survey Monkey R questionnaire and Google Forms R were sent to 10,055 orthopedicians, during the 7 th week of nationwide lockdown in the month of May, 2020. Complete responses were received from 407 participants who were included in the final analysis. Results: Only a quarter ( n = 100/407, 24.6%) of the doctors were visiting hospitals at a frequency similar to that before the pandemic. Onus of orthopedic care among COVID-19 suspected/diagnosed cases and routine COVID-19 patients were mainly borne by the government sector. Only 38.8% ( n = 158/407) doctors felt that they received adequate training while 64.9% ( n = 264/407) of the doctors had adequate supply of PPE kits. “Designation of specific areas of their hospitals exclusively for COVID-19 patients” was opined by 43.7% ( n = 178/407), urgent diagnostic facilities by 52.8% ( n = 215/407), and exclusive operation theaters by only 28.3% ( n = 115/407) of the respondents. Conclusion: This study has shown a drastic fall in the frequency of hospital visits by orthopedic surgeons, predominant involvement of government sector orthopedicians, inadequate training of doctors and inadequate availability of PPE kits, lack of proper designated areas, operation theaters, and urgent diagnostic facilities for the management of COVID-19 patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".