Decreases in Elective and Non-Elective Surgical Case Volumes During the COVID-19 Pandemic
Bibliographic record
Abstract
Background: The coronavirus disease 2019 (COVID-19) pandemic has had an unprecedented impact on surgical healthcare delivery systems. Multiple surgical organizations outlined recommendations on the performance of surgeries to minimize viral transmission, prioritize resource allocation, and avoid perioperative complications. This study aims to characterize the changes in surgical volume during the COVID-19 pandemic. Methods: A retrospective chart review was performed at a large public hospital to characterize the surgical case volume, specialties performing surgeries, case urgency (elective vs. non-elective), patient presentation (emergency room, clinic, inpatient), and patient demographics. Data were collected between January 17 and May 8, 2020, 8 weeks prior to and 8 weeks after the declaration of COVID-19 as a national emergency in the USA. For comparison, data between January 17 and May 8, 2019 were also collected. A univariate analysis was performed via paired tests between the two years. Results: There was a statistically significant decrease in both elective and non-elective cases in 2020. When compared to 2019, the weekly case volume in 2020 is significantly higher prior to the declaration of COVID-19 a national emergency (weeks 1 - 8) and significantly lower after the declaration of COVID-19 as a national emergency (weeks 9 - 16). Additionally, there appeared to be statistically significant decrease in non-elective surgical case volumes. Conclusions: In facing the challenges presented by the COVID-19 pandemic, clinician leaders have been tasked with making difficult decisions regarding patient care. While leading surgical organizations provided guidelines for best practices at the start of the pandemic, the long-term implications of these decisions are unknown. This study has found that the COVD-19 pandemic has resulted in decreased volume of both elective and non-elective surgeries, raising concerns that necessary care may be delayed for marginalized populations. J Curr Surg. 2021;11(4):73-81 doi: https://doi.org/10.14740/jcs452
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".