Improved care and management of paediatric neurological patients evaluated at a paediatric Rapid Access Neurology clinic: A pilot study
Bibliographic record
Abstract
AIM: Referral wait times for paediatric neurological patients are increasing, leading to an increased burden on the emergency department (ED). The paediatric Rapid Access Neurology (pRAN) clinic was created for paediatric patients who are clinically stable, but require an urgent paediatric neurology consultation. The objectives were to evaluate the pathways of referral, accuracy of referring diagnoses, adherence to clinic appointments, impact of clinic visitation on ED visits and patient satisfaction. METHODS: Data were collected from the pRAN clinic from March 2018 until April 2019. Information was obtained from patient charts including the referring and final diagnosis, management plan and the number of visits made to the ED before and after visiting the pRAN clinic. RESULTS: Of the 256 referred patients, 91 met inclusion criteria. The most frequent referral diagnosis was a seizure. Referring physicians and pRAN clinic neurologists differed significantly in the level of diagnostic agreement for patients <2 years of age (P = 0.03; 95% confidence interval (CI) -0.294, 0.373). There was a significant reduction in visits to the ED made by patients 3 months after the pRAN appointment compared with before the visit (P < 0.001; 95% CI -0.9070, -0.4088). The majority of patients felt that the clinic had high value and were satisfied with their follow-up plan. CONCLUSION: This pilot study showed that a pRAN clinic can improve the accuracy of neurological diagnoses and management, especially for children <2 years of age. In addition, pRAN clinic patients make fewer subsequent visits to the ED and express high satisfaction with their 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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; 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".