A multiaxial evaluation of the headache patient
Bibliographic record
Abstract
Background: Primary headaches are considered a complex medical problem. They usually appear as isolated episodes but can progress into chronic headaches entailing significant functional disability for the patient. With the objective of upgrading the quality of care given to headache patients, there have been several proposals to integrate the wide array of variables which influence headache experiences into a systemized evaluation model. Such a system should prevent key elements from being overlooked, aid diagnosis and facilitate treatment plans. However, as of yet, no such model has been widely adopted. Method: In the present paper, we propose integrating The International Classification of Headache Disorders (ICDH) into a multiaxial assessment system similar to the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) which is used in psychiatry. The contents of the different axes found in the DSM cover many of the fundamental clinical variables which have been supported by the medical literature for the past twenty years. Our discussion focuses mainly on chronic headache and migraine since they are clinically relevant to this form of evaluation. We believe our proposed model could be applied generally to all headache types. Conclusion: Headache disorders require an evaluation method flexible enough to reflect the multiple dimensions influencing the course of the disease. In order to achieve a systemized, widely accessible evaluation, we propose a headache patient evaluation structure that is familiar and generally accepted by the medical community. Implementing such a system would be beneficial as it could lead towards building a more uniform evaluation system, facilitate student learning and communication among practitioners, all of which are important steps for improving patient 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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".