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Record W2897652073 · doi:10.1136/jnnp-2018-abn.33

WED 080 Experiences of specialist referral and GP access to MRI for headache

2018· article· en· W2897652073 on OpenAlexaff
McKinlay Alison, Raphael Underwood, Mazumder Asif, Rachael Kilner, Ridsdale Leone

Bibliographic record

VenueJournal of Neurology Neurosurgery & Psychiatry · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMedical Research and Practices
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsWorryThematic analysisReferralMedicineReceiptFamily medicineMagnetic resonance imagingPediatricsQualitative researchPsychiatryRadiologyAnxiety

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.172
GPT teacher head0.514
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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