The impact of resource allocation during the COVID-19 pandemic on cardiac surgical practice and patient outcomes: a systematic review
Bibliographic record
Abstract
OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic has shaken the world and placed enormous strain on healthcare systems globally. In this systematic review, we investigate the effect of resource allocation on cardiac surgery programs and the impact on patients awaiting elective cardiac surgery. METHODS: PubMed and Embase were systematically searched for articles published from 1 January 2019 to 30 August 2022. This systematic review included studies investigating the impact of the COVID-19 pandemic on resource allocation and the subsequent influence on cardiac surgery outcomes. A total of 1676 abstracts and titles were reviewed and 20 studies were included in this review. RESULTS: During the COVID-19 pandemic, resources were allocated away from elective cardiac surgery to help support the pandemic response. This resulted in increased wait times for elective patients, increased rates of urgent or emergent surgical intervention and increased rates of mortality or complications for patients awaiting or undergoing cardiac surgery during the pandemic. CONCLUSIONS: While the finite resources available during the pandemic were often insufficient to meet the needs of all patients as well as the influx of new COVID-19 patients, resource allocation away from elective cardiac surgery resulted in prolonged wait times, more frequent urgent or emergent surgeries and negative impacts on patient outcomes. Understanding the impacts of delayed access to care with regards to urgency of care, increased morbidity and mortality and increased utilization of resources per indexed case needs to be considered to navigate through pandemics to minimize the lingering effects that continue to negatively impact patient outcomes.
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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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".