Lessons Learned From the Pandemic: A Retrospective Analysis of the Impact of COVID-19 in the Routine of a Reference Center for Spine Surgery
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic significantly impacted the operating room schedules and reduced spine-related surgical activity around the world. With the widespread adoption of vaccination and the gradual easing of restrictions in Canada, the healthcare system has largely returned to its pre-pandemic operational capacity. METHODS: This retrospective study evaluated spine surgery activities at Centre Hospitalier de l'Université de Montréal (CHUM), a tertiary academic hospital in Canada. Three continuous but distinct periods were analyzed: pre-pandemic (P1) extended from March 2018 to February 2020; pandemic period (P2), from March 2020 to June 2021; and post-vaccination period (P3), from July 2021 to September 2022. Data on surgical volume, type of surgery (scheduled vs. emergency), mean preoperative waiting time, complication rates, and 30-day mortality rates were compared across the three periods. RESULTS: A total of 1,775 surgeries were performed during the study period. The monthly surgical volume averaged 34.6 in P1 (820 in total), decreased to 33.8 in P2 (540 in total), and rebounded to 37.7 in P3 (415 in total). Emergency surgeries accounted for 31% of cases in P1, increased to 52% in P2, and decreased to 35% in P3 (p = 0.03) (256, 281, and 144, respectively). Complication rates declined from 10.1% in P1 to 5.6% in P2 and then rose to 10.6% in P3 (p < 0.01) (83, 30, and 44, respectively). The mean preoperative waiting times were 131.3 days in P1, 118.4 days in P2, and 178.8 days in P3 (p < 0.01). The 30-day mortality rate remained less than 1% across all periods. CONCLUSION: This work showed the steady volume of surgical activity during the pandemic in a tertiary center. This phenomenon happened largely due to the change in profile in surgical cases. Non-elective cases became more frequent, and elective patients were sparse, probably demonstrating the necessity to deal with cases that otherwise would be treated in community hospitals. Despite this fact, the complication rate decreased during the pandemic, and the mortality rate stayed stable. As the application of the vaccine in the Canadian population, elective spine surgery activity levels were successfully restored.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".