F.6 Opportunities for improvement: understanding drivers of emergency department visits within 90 days of posterior spinal decompression surgery
Bibliographic record
Abstract
Background: Canadian Emergency Departments (EDs) are overburdened. Understanding the drivers for postoperative patients to attend the ED allows for targeted interventions thereby reducing demand. We sought to identify “bounce back” patterns for subsequent QI initiatives. Methods: From April 1, 2016 to March 31, 2022, all provincial ED datasets (EDIS, STAR, Meditech) identified patients presenting within 90 days post-spine surgery. Using Canadian Classification of Health Interventions codes, laminectomies (1SC80) and discectomies (1SE87) demonstrated the highest ED visit rates. Comprehensive chart reviews were conducted identifying surgical and medical reasons for presentation within this timeframe. Results: Reviewing a cohort of 2165 post-decompression patients, 42.1% presented to the ED (n=912) with 62.8% of these directly related to surgery. Primary reasons included wound care (31.6%), pain management (31.6%), and bladder issues (retention or UTI, 11.0%). Simple wound evaluation constituted 49.7% of wound-related visits, with surgical site infection 37.6% and dehiscence 6.6% accounting for the remainder. Pain-related presentations resulted in 72.3% discharge with additional medications, and 27.7% necessitating hospital admission. New or worsening neurologic deficits were reported in 8.9% of ED visits. Conclusions: These findings illuminate crucial aspects of postoperative care and ED utilization patterns. Prioritizing patient education, pain management, and wound care could help alleviate the national ED crisis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".