The impact of COVID-19 on surgical activity
Bibliographic record
Abstract
<p><strong>Aim </strong></p>\n<p>This study aims to examine the impact of COVID-19 on surgical activity in a Model 3 Hospital. </p>\n<p><strong>Methods </strong></p>\n<p>A retrospective, observational study assessing data collected over a 3-month period (February to April) in 2019 and 2020. </p>\n<p><strong>Results </strong></p>\n<p>There was an overall reduction in surgical activity between 2019 and 2020. This impact was felt most acutely in the month of April where elective theatre and endoscopy procedures fell from 131 to 9 (93%) and 399 to 102 (74%) respectively. The number of emergency department admissions reduced from 534 to 408 (24%) and the number requiring surgical intervention fell from 166 to 122 (27%). Attendance at surgical outpatients fell from 1,211 to 677 (44%) between the 2019 and 2020. In April, attendance reduced from 456 to 52 (86%). </p>\n<p><strong>Discussion </strong></p>\n<p>This study has quantified the reduction in surgical department activity in our Model 3 Hospital. This reduction in scheduled and non-scheduled care could be extrapolated nationally to inform service planning, which will become increasingly challenged unless action to address the service deficit is taken soon.</p>
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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 | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.051 | 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 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".