P.137 Neurosurgical consultations in Nova Scotia: a descriptive analysis
Bibliographic record
Abstract
Background: Receiving and managing neurosurgical consultations are central to providing quality patient care but are resource intensive processes. As part of an ongoing quality improvement initiative, we conducted a single-institution descriptive analysis of adult neurosurgical consultations. Methods: A retrospective review of prospectively collected consultation records and call schedules from a 12-month period from February 2019 to 2020 was performed. Consults were graded according to disposition (admission for surgery, non-operative admission, additional investigations recommended, opinion without further investigations, unnecessary consult). Results: There were 1916 consultations reviewed, with 52% of calls (n=991) originating outside of our hospital, and 72% (n=1387) coming from an emergency department. Cranial cases made up 64% (n=1230) of consults, while the remaining 36% (n=688) were spine cases. The mean patient age was 60.1±0.4 years. In multinomial logistic regression analysis, age, geographical distance of consulting site, and consult specific variables (neurosurgical subspecialty, inside vs. outside call, emergency department vs. inpatient ward or private office) were associated with consult disposition ( p < 0.001). Conclusions: This study provides a descriptive analysis of neurosurgical consultations in Nova Scotia. Results from this study may be used to address inefficacies in the neurosurgical consultation process, including targeted education for consulting physicians.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".