F.6 Quality improvement in neurosurgery: the dramatic impact of the spine assessment clinic in reducing post-op emergency department visits
Bibliographic record
Abstract
Background: Our prior six-year review (n=2165) revealed 24% of patients undergoing posterior decompression surgeries (laminectomy or discectomy) sought emergency department (ED) care within three months post-surgery. We established an integrated Spine Assessment Clinic (SAC) to enhance patient outcomes and minimize unnecessary ED visits through pre-operative education, targeted QI interventions, and early post-operative follow-up. Methods: We reviewed 13 months of posterior decompression data (n=205) following SAC implementation. These patients received individualized, comprehensive pre-operative education and follow-up phone calls within 7 days post-surgery. ED visits within 90 days post-surgery were tracked using provincial databases and compared to our pre-SAC implementation data. Results: Out of 205 patients, 24 (11.6%) accounted for 34 ED visits within 90 days post-op, showing a significant reduction in ED visits from 24% to 11.6%, and decreased overall ED utilization from 42.1% to 16.6% (when accounting for multiple visits by the same patient). Early interventions including wound monitoring, outpatient bloodwork, and prescription adjustments for pain management, helped mitigate ED visits. Patient satisfaction surveys (n=62) indicated 92% were “highly satisfied” and 100% would recommend the SAC. Conclusions: The SAC reduced ED visits after posterior decompression surgery by over 50%, with pre-operative education, focused QI initiatives, and its individualized, proactive approach.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".