Impact of the COVID-19 pandemic on non–small-cell lung cancer pathologic stage and presentation
Bibliographic record
Abstract
BACKGROUND: It is believed that the cessation of normative cancer care services during the COVID-19 pandemic may be resulting in pathologic upstaging and higher long-term mortality rates. We aimed to understand how the pandemic has affected our patients diagnosed with non-small-cell lung cancer (NSCLC). METHODS: We conducted a single-centre retrospective analysis to assess how the COVID-19 pandemic has affected patient referrals, pathologic stage of NSCLC, mortality rates and surgical procedures at our cancer care centre in Ontario, Canada. At our centre, physicians advocated for and followed recommendations that operations in cancer patients should be among the last procedures to be delayed. Patients were included if they were aged 18 years or older, were not receiving palliative care, and had been screened, diagnosed and treated for NSCLC (primary tumours). We compared outcomes between a prepandemic period (January 2019 to February 2020) and a period during the pandemic (March 2020 to February 2021). RESULTS: = 320) the pandemic. CONCLUSION: Cancer care services at our centre were maintained during the COVID-19 pandemic, and potential adverse effects on prognosis and survival that have been seen in other countries were avoided. The results inform health care providers how the effects of future pandemics can be blunted by using proactive preservative strategies and surgeon advocacy.
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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 | 0.001 | 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) | 0.001 | 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".