Impact of COVID-19 Pandemic on Colonoscopy Wait Times by Procedure Indication
Bibliographic record
Abstract
Background: Patients are referred for colonoscopy for symptom assessment, screening, and surveillance. Public health measures to mitigate the spread of the COVID-19 pandemic disrupted services and increased patient delays for colonoscopy services. The differential impact of these interruptions by colonoscopy indication is largely unknown. We aimed to understand the effects of the pandemic on colonoscopy services and patient wait times in Montreal, Canada. Study: Using 2018-2022 retrospective clinical data from 2 high-volume Montreal endoscopy centres and provincial administrative data, we characterized changes in colonoscopy wait times and the proportion of wait-listed patients who were delayed (wait time exceeded provincial guidelines) by procedure indication and demographics. We used regression to examine patient characteristics associated with delayed procedures during pre- and intraCOVID-19 periods. We used time series analysis to characterize trends in the proportion of wait-listed patients delayed. Results: The COVID-19-related public health measures resulted in record-high delays (median increase in wait times of 34%-159% across indications). While older patients experienced longer wait times pre-pandemic, intra-COVID-19 wait times increased disproportionately for patients younger than 50. The proportion of wait-listed patients delayed peaked in mid-2020 (56.9% for screening; 56.0% for symptom assessment patients). By early 2022, the proportion delayed had fallen to 37.3% for screening patients but remained at 53.8% for symptom assessment patients. Conclusions: Pandemic service disruptions disproportionately impacted symptom assessment procedures and younger patients, resulting in lasting effects. Systematic monitoring of procedures and wait times could facilitate timely detection and intervention to prevent disparities in patient access to care.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".