P.073 Improving access to urgent neurology care for pediatric patients
Bibliographic record
Abstract
Background: Pediatric neurology referral wait times are increasing, often leading to emergency department (ED) utilization. On average 5% of ED patients present with neurological symptoms and 35% of ED neurological diagnoses are revised after specialist review. A Stollery Rapid Access Neurology (RAN) clinic was created to decrease wait time, and initiate an efficient referral process. Methods: The RAN clinic ran weekly from March 2018 until February 2019. This was a prospective study approved by the University of Alberta ethics board. Inclusion criteria were met. Information was collected for diagnosis, along with confidential patient satisfaction surveys. Results: Seventy-five patients were referred, 49% from the ED. Wait time averaged 6 weeks. The most frequent referral reason was seizures, with 60% of referring diagnosis being correct. Prior to RAN appointment, 61% of patients presented to the ED, whereas only 0.1% returned in the following 3 months. Neurology follow up was required in 81% of patients. Overall satisfaction was ranked 9.6/10. Conclusions: The RAN clinic created an effective urgent triage method. Neurologist review revised 40% of diagnoses. This ongoing study reveals that a RAN clinic can reduce visits to the ED following appointment and initiate appropriate follow up. Future evaluation in cost effectiveness and telehealth appointments are required.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.003 |
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".