COVID-19 pandemic effects on orthopaedic surgeons in British Columbia
Bibliographic record
Abstract
BACKGROUND: COVID-19 was declared a global emergency in the first quarter of 2020. It is has resulted in and continues in over a million deaths worldwide and halted medical systems and particularly elective surgeries worldwide. The aim of this study was to identify the effect of the initial COVID-19 pandemic months on orthopaedic surgeons in British Columbia. METHODS: The study surveyed (June until August 2020) 187 orthopaedic surgeons in British Columbia affiliated with the University of British Columbia or the British Columbia Orthopaedic Association using an online survey to help identify the impact of COVID-19 on patient care, work and personal life. RESULTS: Eighty-seven out of 187 (46.5%) orthopaedic surgeons participated in the online survey. All types of surgeries were completely cancelled for 23 respondents. Elective surgeries were cancelled for most respondents (in-hospital n = 38 and/or ambulatory n = 32). Trauma cases were reduced according to 35 respondents. Outpatient clinics were stopped initially and transferred in virtual clinics (telemedicine). Approximately 40% of respondents were afraid of infecting others (patients, family) and 25% admitted to drink more heavily. Ninety percent of respondents reported an income loss of > 15% (range 0-100%). CONCLUSION: Orthopaedic surgeons and their patients have been significantly affected by the COVID-19 pandemic. Cancellation of surgeries has created an increased backlog of 32,400 orthopaedic surgeries in British Columbia. However, the COVID-19 pandemic has expedited the implementation of telemedicine, which will be a long-lasting benefit in providing healthcare.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".