Influence of the COVID-19 Pandemic on Clinical Trial Discontinuation in Anesthesiology: Cross-sectional Analysis
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic drastically altered perioperative medical practice owing to safety concerns, postponing elective or nonemergent procedures, supply chain shortages, and reallocating perioperative staff to care for patients with COVID-19. However, the impact of the pandemic on the conduct on anesthesiology clinical research is unknown. OBJECTIVE: The primary objective was to quantify the magnitude of the COVID-19 pandemic's impact on anesthesiology clinical research. METHODS: We performed a systematic search using ClinicalTrials.gov to identify clinical trials related to the practice of anesthesiology. We screened trials with status updates from January 1, 2020, through October 1, 2021, to capture trials potentially affected by the COVID-19 pandemic by the time of our search. Investigators screened for relevant studies and extracted trial characteristics along with the reason for discontinuation reported on the clinical trial registry. RESULTS: A total of 823 clinical trials met inclusion criteria, and 146 clinical trials were discontinued within the designated date range. In total, 24 (16.4%) of the 146 clinical trials were halted explicitly owing to the COVID-19 pandemic. A significant association existed between trial enrollment numbers and the likelihood of discontinuation due to the COVID-19 pandemic, as larger trials were more likely to be disrupted (z=-2.914, P=.004). CONCLUSIONS: The COVID-19 pandemic is reportedly associated with the discontinuation of anesthesiology-related clinical trials. With the uncertain course of the COVID-19 pandemic, developing anesthesia trial protocols to help minimize social interaction and prevent premature trial disruption are imperative.
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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 | 0.161 | 0.352 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.011 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".