Referrals, Wait Times and Diagnoses at an Urgent Neurology Clinic over 10 Years
Bibliographic record
Abstract
BACKGROUND: An urgent neurology assessment clinic was created at our institution to improve access to prompt neurological assessment, and has been in operation for over a decade. We assessed its timeliness and impact. METHODS: The clinic database was examined retrospectively for trends in the volume and waiting time to assessments, neurologic diagnoses, and whether neurologic assessment changed patients' diagnoses. Before and after implementation, the frequency of emergency department neurology assessments and hospital admissions for neurological investigation were compared. RESULTS: In the first decade, 25145 referrals were received; 12460 patients were accepted and assessed within an average of 3.8 working days. The most common problems seen included headache and seizure (20.2% each). Overall, 44.6% of assessments resulted in a change to the referring diagnosis; this proportion varied by the type of problem seen (from 10.5% for seizures to 92.5% for psychiatric disturbances). From the pre- to post-opening periods, there were fewer emergency room neurological assessments (35.7% reduction) and fewer hospital admissions for neurological investigation (4.4/week to 2.2/week, 50% reduction). CONCLUSIONS: The urgent neurology clinic model at our institution has provided excellent service, including wait times of a few days, to a catchment of over two million Canadians for over a decade; clinic assessments have affected diagnoses and patient care.
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".