Thoracic Surgeon Impressions of the Impact of the COVID-19 Pandemic on Lung Cancer Care—Lessons from the First Wave in Canada
Bibliographic record
Abstract
Background: COVID-19 has invariably changed the way lung cancer surgical care is provided in Canada. Despite relevant management guidelines, the way in which cancer care has been affected has yet to be described for thoracic surgical populations. Routine lung cancer physiologic and staging assessments are unique in that they are droplet producing and aerosolizing procedures. Our objective was to quantify the effect of the COVID-19 pandemic on surgical lung cancer care as perceived by practicing thoracic surgeons during the first wave of the pandemic in Canada. Methods: An electronic survey was distributed to members of the Canadian Association of Thoracic Surgeons. The survey was designed to determine surgeon perception of lung cancer preoperative care during the Canadian pandemic-instilled period of resource reallocation compared to standard care. Planned analyses were exploratory in nature; with count and frequency distributions of responses quantified. Results: Fifty-three thoracic surgeons completed the survey. Responses were collected from all Canadian provinces. Little change in access to preoperative imaging was noted. However, a significant decrease in access to lung function and bronchoscopy testing occurred. Pulmonary surgery was perceived to be lengthier with reduced operating theater availability. Despite decreased OR access, only 40% of surgeons were aware of respective institutional mitigation strategies. Summary: The COVID-19 pandemic has had an impact on standard lung cancer care preoperative workup. Further inquiry using institutional data is warranted to quantify its impact on cancer patient outcomes. Assessing the extent and effects of newly present barriers to standard lung cancer care is essential in forming appropriate mitigation strategies and planning for future pandemic waves.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".