The Changing Face of Cancer Surgery During Multiple Waves of COVID-19
Bibliographic record
Abstract
COVID-19 has had a detrimental effect on the provision of cancer surgery, but its impact beyond the first 6 months of the pandemic remains unclear. We used data on 799 220 cancer surgeries performed in Ontario, Canada, during 2018-2021 and segmented regression to address this knowledge gap. With the arrival of the first COVID-19 wave (March 2020), mean cancer surgical volume decreased by 57%. Surgical volume then rose by 2.5% weekly and reached prepandemic levels in 8 months. The surgical backlog after the first wave was 47 639 cases. At the beginning of the second COVID-19 wave (January 2021), mean cancer surgical volume dropped by 22%. Afterward, surgical volume did not actively recover (2-sided P = .25), resulting in a cumulative backlog of 66 376 cases as of August 2021. These data urge the strengthening of the surgical system to quickly clear the backlog in anticipation of a tsunami of newly diagnosed cancer patients in need of surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".