Trends and variations in Canadian thoracic surgical volume and perioperative practice during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Thoracic surgeons in Canada responded to the COVID-19 pandemic without existing precedence. The primary aim of this study was to understand how thoracic surgery care in Canada was affected by the pandemic in terms of volume, perioperative management, and patterns of practice. METHODS: Data were obtained using 2 questionnaires (18-item surgeon-specific and 13-item institution-specific questionnaires) in addition to the Canadian Association of Thoracic Surgery (CATS) national database. Outcomes included qualitative surgeon experiences and thoracic surgery volume from March 2020 to December 2022. Centres were separated into 3 levels of COVID-19 burden based on community prevalence. RESULTS: We received survey responses from 63 surgeons and 6 institutions. In-person consultation dropped by 57% during the pandemic. Preoperative cancer workups experienced minor (≤ 4 wk, 39%) and major (≥ 8 wk, 27%) delays. Operable lung and esophageal cancer experienced minor delays in treatment, while pure ground-glass opacities and benign esophageal pathology experienced major delays (25%) or cancellations (21%). Medical education shifted to virtual platforms, decreasing student involvement by 81%. Perceived factors affecting operating room availability included lack of staff, beds, and personal protective equipment. CONCLUSION: There was a pan-Canadian reduction in thoracic surgery volume, regardless of regional COVID-19 caseload. Prioritization of thoracic oncology was observed, with a delay in care for minimally invasive and benign illness. Our findings illustrate how surgeons and institutions responded to the pandemic and inform strategies for Canadian thoracic practice in the event of future analogous events.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".