Streamlining lung cancer management in Nova Scotia amid COVID-19: pooled triaging for expedited curative-intent oncologic surgery
Bibliographic record
Abstract
Background: The effect of the COVID-19 pandemic on the diagnosis and management of lung cancer in Canada is not fully understood. We sought to quantify the provincial volume of diagnostic imaging, thoracic surgeon referrals, time to surgery after referral, and pathologic staging for curative surgery in the context of the pandemic, as well as explore the effect of a pooled patient model, which was implemented to prioritize surgeries for lung cancer and mitigate the effects of the pandemic. Methods: We conducted a retrospective cohort study of patients who underwent diagnostic imaging in Nova Scotia and were subsequently referred to a thoracic surgeon at the province’s only tertiary care centre for surgical management of their primary lung cancer before (Mar. 1, 2019, to Feb. 29, 2020) and during (Mar. 1, 2020, to Feb. 28, 2021) the COVID-19 pandemic. We conducted a survey to capture the patient and surgeon experience with a pooled patient model of managing surgical oncology cases. Results: Compared with the pre-COVID-19 period, the overall volume of chest radiography and chest computed tomography decreased by 30.9% (p < 0.001) and 18.7% (p = 0.002), respectively, in the COVID-19 period. Thoracic surgeon referrals, operative approach, extent of resection, length of hospital stay, and pathologic staging did not significantly differ. Time from referral to surgery was significantly shorter during the COVID-19 period (mean 196.8 d v. 157.9 d, p = 0.04). A pooled patient approach contributed to positive patient satisfaction. Conclusion: The COVID-19 pandemic was associated with reductions in rates of diagnostic imaging and referrals to thoracic surgeons for management of pulmonary cancer. A pooled patient model was used to mitigate the effects of the pandemic on lung cancer management and was positively received by patients. An extended study period is needed to determine the full effect of this redistribution of resources.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".