Effectiveness of a Multidisciplinary Treatment Program for Chronic Daily Headache
Bibliographic record
Abstract
BACKGROUND: Chronic daily headache (CDH: headache on fifteen days a month or more) is one of the most common forms of chronic pain. The relative efficacy of different treatment methods for these patients needs to be determined. OBJECTIVE: To compare treatment outcomes for patients with CDH treated in a traditional office-based pharmacological treatment program with a second group treated in a multidisciplinary management program. METHODS: Patient outcomes were measured using changes in the Headache Disability Inventory (HDI) and the Short-Form-36 (SF-36) over the treatment period. Outcomes from seventy patients treated in an office setting were compared to thirty-seven patients treated in a multidisciplinary headache treatment program. Both groups received similar pharmacological treatment. All patients treated in the office setting and the majority of patients in the multidisciplinary program had transformed migraine. RESULTS: Even though a reduction in headache days per month occurred, mean headache related disability (measured by HDI) and mean Health Related Quality of Life (HRQoL measured by SF-36) did not improve for the patient group treated in the office setting but did improve significantly for the patient group treated in the multidisciplinary headache program. CONCLUSION: For patients with CDH, headache-related disability and HRQoL is more likely to improve with management in a multidisciplinary headache treatment program as compared to the traditional specialist consultation-family physician office-based setting.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".