A new GP with special interest headache service: observational study
Bibliographic record
Abstract
BACKGROUND: There is poor access to neurology services for patients in the community. AIM: To describe the training of GPs with special interest (GPwSI) in headache and the setting up of a GPwSI clinic in general practice, and report on a comparison with the existing neurology service in terms of case severity, patient satisfaction, and cost. DESIGN OF STUDY: New service provision and evaluation by a questionnaire survey. SETTING: General practice and hospital neurology service in inner-city London. METHOD: The intervention involved training GPs as GPwSIs and setting up a GP headache service. A questionnaire survey was conducted, measuring headache impact, satisfaction, and cost estimates. RESULTS: Headache impact was not significantly different between the two groups of patients, referred to hospital and to a GPwSI. Patients were significantly more satisfied with the GPwSI service, particularly that the service was effective in helping to relieve their symptoms (89% versus 76%; adjusted odds ratio=7.7; 95% confidence interval=2.7 to 22.4). The cost per first appointment was estimated to be pound sterling 136, with pound sterling 68 for subsequent contacts. These are lower than costs for neurologist contacts. CONCLUSION: GPwSI services can satisfy the needs of patients with similar headache impact at costs that are lower than those for secondary care services.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".