Cervical Manipulation and Informed Consent: Canadian Manipulative Physiotherapists' Opinions on Communicating Risk
Bibliographic record
Abstract
Purpose: The study objective was to generate an information sheet for Canadian manipulative physical therapists (CMPTs) to use when seeking informed consent for high-velocity, low-amplitude cervical manipulation. Methods: A cervical manipulation information sheet (CMIS) was created with five sections: Introduction, Benefits, Risks, Procedures and Effectiveness. The content of the information sheet was generated using the Delphi method, followed by a mail-out survey to a random sample of CMPTs (N = 307) to determine the information sheet's acceptability and clinical utility. The proportion of CMPTs who agreed with the content of the information sheet and the proportion of CMPTs who indicated a willingness to use the sheet clinically were calculated. Results: The survey response rate was 74 per cent. The proportion (95 per cent confidence interval) of respondents who agreed with the content of the CMIS and approved its clinical acceptability was 0.95 (0.94-0.96) and 0.61 (0.58-0.64), respectively. Written comments from the CMPTs reflected concern about wording in the Risks section. Conclusions: Our results suggest that CMPTs agreed with the content of the CMIS but not how it was written. Physical therapists should consider the CMIS a proposed guideline for clinician use. Ideally, a patient version should also be created.
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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 | 0.156 | 0.293 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| 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".