Rescheduling of Cancelled Elective Surgical Procedures Among Older Adults Post–COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic has recently put a stop to elective surgical procedures across Canada, inherently compounding already lengthy waitlists that exist within most disciplines of surgery. These long waits for elective procedures within Canadian provinces have not been caused by the COVID-19 pandemic; it is an acute-on-chronic issue that has been exacerbated by the ongoing COVID-19 pandemic. As hospitals begin to reschedule elective surgeries, patients are likely to be prioritized by clinical urgency using both established and newly created surgical triage severity scales. The objective of this commentary is to discuss issues related to the rebooking of elderly surgical patients during the COVID-19 pandemic within the context of northern medicine. Northern and rural hospitals may already face a multitude of barriers related to the rebooking of surgical patients due to a paucity of available surgical resources, as well as difficulties related to accessing care at the local level. While current surgical rebooking tools have been developed in response to the COVID-19 pandemic, they fail to explore certain risks related to the older adult population which may lead to increased mortality and morbidity. Review of the literature indicates that redistribution of surgical resources for older adults in the COVID-19 era will require consideration of clinical medical ethics vs. population health ethics regarding who should be prioritized in re-bookings for elective surgical procedures. This should be done in conjunction with encompassing surgical triage severity scales specifically made for older adults in the time of COVID-19.
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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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".