The Impact of the COVID-19 Pandemic on Case Volume and Wait Times of Elective Hand Procedures: A Retrospective Chart Review Study
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic has decreased the number of surgeries performed in North America. The purpose of this study was to compare the number of elective hand surgeries performed during the pandemic to a corresponding pre-pandemic time period and to quantify the impact to the surgical backlog in hand surgery. Methods: Patient health records for individuals who underwent surgical management of carpal tunnel syndrome (CTS), Dupuytren's disease (DD) or stenosing tenosynovitis (time periods: March 11, 2018 to July 1, 2019 [pre-pandemic] and March 11, 2020 to July 1, 2021 [pandemic]) were retrieved from two academic institutions. The primary outcome was number of surgeries performed in each time period. Secondary outcomes included wait times for each time period; and variables as predictors of wait times, including a) age; b) gender; c) socioeconomic status; d) geographic location; and, e) comorbidities. Results: Seven-hundred-and-fifteen cases were included (447 CTR cases, 135 fasciotomy/subtotal palmar fasciectomy cases and 133 pulley release/tendon release cases). Two-hundred-and-sixty-four elective hand procedures were performed during the COVID-19 time period, compared to 451 in the pre-pandemic time period (n = 187 surgeries, 41.5%). Mean surgical wait times decreased for CTS and DD and increased for stenosing tenosynovitis during the pandemic compared to the corresponding pre-pandemic time period. No association or variation in wait times was found in regard to the aforementioned variables. Conclusions: During the pandemic, a decreased total number of elective hand surgeries were performed when compared to the corresponding pre-pandemic period. This contributes to a backlog of elective surgical procedures.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".