Examining the surgical backlog due to COVID-19 in Latin America and the Caribbean: insights from a scoping review
Bibliographic record
Abstract
This scoping review assessed the surgical backlog in Latin America and the Caribbean (LAC) due to COVID-19 and identified mitigation strategies. We searched seven databases for citations from December 2019 to December 2022, focusing on LAC patients with cancelled or postponed procedures. We registered our protocol at Open Science Framework (https://osf.io/x2nd8) and adhered to PRISMA-ScR guidelines. We included 83 citations covering 23 LAC countries and 19 surgical specialities, with Brazil (67%, 56/83) and transplant surgery (24%, 20/83) being the most documented. Surgical backlogs were mainly reported at the hospital (44%, 37/83) and national levels (38%, 32/83). We identified 58 citations that reported a total of 42 strategies to mitigate the backlog, the most cited being establishing prioritisation criteria for surgical cases (41%, 24/58). Our findings highlight challenges across differing healthcare systems in LAC, including disparities in data availability, surgical capacity, and resource allocation. For instance, while countries like Brazil had extensive data on national surgical backlogs, others lacked comprehensive national-level data. Our review can help inform policymakers and healthcare stakeholders to implement targeted interventions to prepare LAC-based surgical systems for future health emergencies.
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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 | 0.024 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.027 | 0.035 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".