Surgery & COVID-19: A rapid scoping review of the impact of COVID-19 on surgical services during public health emergencies
Bibliographic record
Abstract
ABSTRACT Background Healthcare systems globally have been challenged by the COVID-19 pandemic, necessitating the reorganization of surgical services to free capacity within healthcare systems. Objectives To understand how surgical services have been reorganized during and following public health emergencies, and the consequences of these changes for patients, healthcare providers and healthcare systems. Methods This rapid scoping review searched academic databases and grey literature sources to identify studies examining surgical service delivery during public health emergencies including COVID-19, and the impact on patients, providers and healthcare systems. Recommendations and guidelines were excluded. Screening was completed in partial (title, abstract) or complete (full text) duplicate following pilot reviews of 50 articles to ensure reliable application of eligibility criteria. Results One hundred and thirty-two studies were included in this review; 111 described reorganization of surgical services, 55 described the consequences of reorganizing surgical services and six reported actions taken to rebuild surgical capacity in public health emergencies. Reorganizations of surgical services were grouped under six domains: case selection/triage, PPE regulations and practice, workforce composition and deployment, outpatient and inpatient patient care, resident and fellow education, and the hospital or clinical environment. Service reorganizations led to large reductions in non-urgent surgical volumes, increases in surgical wait times, and impacted medical training (i.e., reduced case involvement) and patient outcomes (e.g., increases in pain). Strategies for rebuilding surgical capacity were scarce, but focused on the availability of staff, PPE, and patient readiness for surgery as key factors to consider before resuming services. Conclusions Reorganization of surgical services in response to public health emergencies appears to be context-dependent and has far-reaching consequences that must be better understood in order to optimize future health system responses to public health emergencies. ARTICLE SUMMARY Strengths and limitations of the study This rapid scoping review provides an exhaustive and rigorous summary of the academic and grey literature regarding modifications to surgical services in response to public health emergencies, especially COVID-19. This study did not limit studies based on location or language of publication to ensure a worldwide pandemic had contributions from worldwide voices. Both quantitative and qualitative outcomes were included, with a mix of inductive and deductive data abstraction approaches to provide a comprehensive understanding of surgical services during public health emergencies. Studies with potential relevance to this question are emerging at an unprecedented rate in response to the COVID-19 pandemic and as such, some may not be included in the current review. Original protocol for the study As requested, the original unpublished protocol for this study is included as a supplementary file. Funding statement This study did not receive grant from any funding agency in the public, commercial or not-for-profit sectors. Competing interest statement All authors declare that they have no competing interests in accordance with the International Committee of Medical Journal Editors uniform declaration of competing interests.
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 | 0.015 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.031 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 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".