The relative effectiveness of cervical spine manipulation and a nonsteroidal anti-inflammatory drug (Ibuprofen) in the treatment of episodic tension-type headaches
Bibliographic record
Abstract
The 1 year overall prevalence of Episodic Tension-Type Headache (ETTH) is 38.3%; with lifetime prevalence at 46% for TTH. Little literature exists to support the effectiveness of spinal manipulation in the treatment of ETTH. Therefore aim of this study was to determine the relative effectiveness of cervical spine manipulation and a Nonsteroidal Anti-inflammatory drug (NSAID) (Ibuprofen®) in the treatment of ETTH. Method: This study was a prospective randomised clinical trial with two intervention groups (N=32, n1=16 and n2=16). The allocation of participants to the two groups was completed by means of simple randomization. Group one were treated using cervical spine manipulation. Group two were treated using Ibuprofen. Subjective measurements included the Numerical Rating Scale 101 Questionnaire (NRS-101), Short Form McGill Pain Questionnaire (SF-MPQ), CMCC Neck Disability Index (CMCC) and Headache Diary. A p value <0.05 was considered as statistically significant. Results: The subjective measurements of the NRS-101, SF-MPQ and CMCC showed a significant time effect in both treatment groups. Several of the subjective Headaches Diary outcomes followed this trend with significant time effect in both groups. There was a significant treatment effect for the NRS-101. Several subject outcomes from the Headache Diary showed a significant treatment effect in favour of manipulation, namely frequency and duration of headaches. Conclusion: The findings in this study have shown that cervical spine manipulation is more effective than Ibuprofen® for the treatment of ETTH in terms of several subjective outcomes namely: pain intensity (NRS-101), and the frequency and the duration of headache per day.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".