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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".