Ontario wait times for delayed surgical treatment of traumatic peripheral nerve injury
Bibliographic record
Abstract
Background: To better understand the occurrence and operative treatment of peripheral nerve injury (PNI) and the potential need for additional resources, it is essential to define the frequency and distribution of peripheral nerve procedures being performed. The objective of this study was to evaluate Ontario’s wait times for delayed surgical treatment of traumatic PNI. Methods: We retrieved data on wait times for peripheral nerve surgery from the Ontario Ministry of Health and Long-Term Care Wait Time Information System. We reviewed the wait times for delayed surgical treatment of traumatic PNI among adult patients (age ≥ 18 yr) from April 2009 to March 2018. Data collected included total cases, mean and median wait times, and demographic characteristics. Results: Over the study period, 7313 delayed traumatic PNI operations were reported, with variability in the case volume distribution across Local Health Integration Networks (LHINs). The highest volume of procedures (2788) was performed in the Toronto Central LHIN, and the lowest volume (< 6) in the Waterloo Wellington and North Simcoe Muskoka LHINs. The population incidence of traumatic PNI requiring surgery was 5.1/10 000. The mean and median wait times from surgical decision to surgical repair were 45 and 27 days, respectively. Both the longest and shortest wait times occurred in LHINs with low case volumes. The provincial target wait time was met in 93% of cases, but women waited significantly longer than men (p < 0.001). Conclusion: The provincial distribution of traumatic PNI surgery was variable, and the highest volumes were in the LHINs with large populations. The provincial wait time strategy for traumatic PNI surgery is effective, but women waited longer than men. Precise reporting from all hospitals is necessary to accurately capture and understand the delivery of care after traumatic PNI.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".