Surgery and COVID-19: a rapid scoping review of the impact of the first wave of COVID-19 on surgical services
Bibliographic record
Abstract
OBJECTIVES: To understand how surgical services have been reorganised during and following public health emergencies, particularly the first wave of the COVID-19 pandemic, and the consequences for patients, healthcare providers and healthcare systems. DESIGN: A rapid scoping review. SETTING: We searched the MEDLINE, Embase and grey literature sources for documents and press releases from governments and surgical organisations or associations. PARTICIPANTS: Studies examining surgical service delivery during public health emergencies including COVID-19, and the impact on patients, providers and healthcare systems were included. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcomes were strategies implemented for the reorganisation of surgical services. Secondary were the impacts of reorganisation and resuming surgical services, such as: adverse events (including morbidity and mortality), primary care and emergency department visits, length of hospital and ICU stay, and changes to surgical waitlists. RESULTS: One hundred and thirty-two studies were included in this review; 111 described reorganisation of surgical services, 55 described the consequences of reorganising surgical services; and 6 reported actions taken to rebuild surgical capacity in public health emergencies. Reorganisations of surgical services were grouped under six domains: case selection/triage, personal protective equipment (PPE) regulations and practice, workforce composition and deployment, outpatient and inpatient patient care, resident and fellow education, and the hospital or clinical environment. Service reorganisations led to large reductions in non-urgent surgical volumes, increases in surgical wait times and impacted medical training (ie, reduced case involvement) and patient outcomes (eg, 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: Reorganisation 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 optimise future health system responses to public 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 | 0.015 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.028 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 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".