The Impact of COVID-19 on the Surgical Wait Times for Plastic and Reconstructive Surgery in Ontario
Bibliographic record
Abstract
Purpose: The aim of this study was to assess the impact of COVID-19 on surgical wait times for Plastic and Reconstructive Surgery (PRS) in Ontario, Canada. Methods: Ontario's wait time data has fourteen reporting categories for PRS. For each category, the mean wait time for consultation and for surgery were reported. Each category was given a priority ranging from 1 to 4. Two periods, three-month and six-month, were selected and compared to the same calendar months of the previous year. Wait times, surgical volume and percent change to the provincial wait time target were reported and compared to the baseline data. Results: This study reviewed 9563 consults and 15,000 operative cases. There was a 50% reduction in the volume of surgical consults during the study period compared to the baseline period (P = 0.004). The reduction ranged from 46% to 75% based on the reporting category. The volume of surgical cases decreased by 43% during the study period compared to the baseline period (P = 0.005). A statistically significant increase in the mean wait times for surgery was observed, involving priorities 2 to 4 (overall mean = 32 days, P ≤ 0.01). There was a 15% decrease in the percentage of surgeries meeting the provincial target times (P < 0.0001). Conclusion: COVID-19 has caused a significant reduction in the volume of cases performed in the majority of PRS categories with an overall increase in the wait times for consultation and for surgery. Recovery following COVID-19 will require strategies to address the growing volume of cases and wait times for surgery across all PRS categories.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".