Pacing as a treatment modality in migraine and tension-type headache
Bibliographic record
Abstract
Purpose: To review the pacing literature; describe the use of pacing in a specialty headache clinic; and provide client feedback regarding the effectiveness of pacing in headache self-management. Method: The evidence for this report was derived from a structured literature review, an established pacing intervention program for patients with headache, and patient self-report questionnaire. Results: There are frequent references to pacing in the chronic pain and rheumatic disease literature, but no universal definition and, until recently, few outcome studies. References to pacing in the headache literature are limited. For a small sub-group of clients at a specialty headache clinic (n = 20), pacing principles taught by occupational therapists were reported to prevent increases in headache intensity (70%); decrease headache intensity (65%), and shorten the duration of a headache (40%). Additionally, 70% of respondents used pacing to prevent headache onset. Pacing was seen to contribute to increased quality of life, headache self-efficacy, function, and independence. There were a variety of opinions regarding the most helpful pacing components. The most frequently endorsed were identify and prioritize responsibilities; balance activity and rest; schedule regular rest breaks; and delegate or eliminate tasks. Conclusions: Pacing appears to play an important role in headache self-management. More pacing research is required in both headache and chronic pain populations.Implications for RehabilitationMigraine and tension-type headaches are associated with significant pain and disability.Overexertion and stress are commonly reported headache triggers.Activity pacing allows individuals with migraine and tension-type headache to self-regulate tasks and activities so they may manage physical exertion and mental stress levels.Pacing may help decrease headache intensity and duration, as well as increase quality of life, function, and self-efficacy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".