WED 080 Experiences of specialist referral and GP access to MRI for headache
Bibliographic record
Abstract
Introduction When General Practitioners (GPs) refer patients with headache to neurologists, it is often because the patient and/or doctor want imaging. Some GPs now access Magnetic Resonance Imaging (MRI) directly. We aimed to describe patients’ experience of GPs using direct access, compared to patients who saw a specialist first. Methods We invited participants to semi-structured Interviews about two months after imaging. Interviews were audio-recorded, transcribed, and analysed using thematic analysis in Nvivo. Results We interviewed 10 patients from each pathway in South London, eleven women, median age 41, range 20–72. We found more similarities than differences between groups. Ten said they received a clear scan result explanation, while six had difficulty understanding results. Eleven participants felt relief once results were received, while five still wanted an answer on the underlying cause for symptoms. Seven felt the specialist appointment wait time was long. Those using the direct-access pathway were more likely to report MRI results delay. Conclusion Patient reassurance was linked closely with results receipt and worry linked with wait times. Some felt MRI results did not provide sufficient explanation for symptoms. Improvement in both pathways can be achieved, providing results are delivered in a clear, timely manner.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".