Immediate and long-term impact of the COVID-19 pandemic on delivery of surgical services
Bibliographic record
Abstract
BACKGROUND: The ongoing pandemic is having a collateral health effect on delivery of surgical care to millions of patients. Very little is known about pandemic management and effects on other services, including delivery of surgery. METHODS: This was a scoping review of all available literature pertaining to COVID-19 and surgery, using electronic databases, society websites, webinars and preprint repositories. RESULTS: Several perioperative guidelines have been issued within a short time. Many suggestions are contradictory and based on anecdotal data at best. As regions with the highest volume of operations per capita are being hit, an unprecedented number of operations are being cancelled or deferred. No major stakeholder seems to have considered how a pandemic deprives patients with a surgical condition of resources, with patients disproportionally affected owing to the nature of treatment (use of anaesthesia, operating rooms, protective equipment, physical invasion and need for perioperative care). No recommendations exist regarding how to reopen surgical delivery. The postpandemic evaluation and future planning should involve surgical services as an essential part to maintain appropriate surgical care for the population during an outbreak. Surgical delivery, owing to its cross-cutting nature and synergistic effects on health systems at large, needs to be built into the WHO agenda for national health planning. CONCLUSION: Patients are being deprived of surgical access, with uncertain loss of function and risk of adverse prognosis as a collateral effect of the pandemic. Surgical services need a contingency plan for maintaining surgical care in an ongoing or postpandemic phase.
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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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".