P.025 Is this headache normal?: Assessing electronic referrals for headache from primary care physicians
Bibliographic record
Abstract
Background: Headache is one of the most frequent complaints in primary care. We reviewed headache questions submitted to an electronic consultation service in Ontario to classify the types of headaches and describe the questions being asked. We also identified reasons why answers were not retrievable within UpToDate, an online clinical resource. Methods: 65 headache eConsults were further divided into 85 questions and categorized by headache type and question theme. Questions were manually searched within UpToDate to determine if they could be answered using this resource. The intent to refer the patient for a face-to-face referral after the eConsult was collected. Results: The top classifications were migraine, unclassified headache, and exertional and/or coital headache. The themes -identified were medication questions (41.7%), investigation questions (33.3%), clinical concerns despite normal neurologic exam and/or imaging (15.5%); and abnormal imaging findings (9.5%). Answers to 40.1% of the questions were not retrievable in UpToDate. The main reason for irretrievability was an unusual presentation. Only 33.8% of eConsults resulted in a face-to-face referral to a specialist. Conclusions: Although electronic resources may be useful in some cases, clinical nuances cannot be accounted for. By providing physicians with rapid access to specialists, eConsult services may obviate the need for formal, face-to-face referrals.
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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 | 0.005 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".