Symptoms patients receiving manual therapy experienced and perceived as adverse: a secondary analysis of a survey of patients’ perceptions of what constitutes an adverse response
Bibliographic record
Abstract
Background: Previous qualitative studies demonstrated that the process by which patients determined whether a response to manual therapy is adverse is very complex. However, it remains unknown which responses to manual therapy patients perceived as adverse.Objective: To describe symptoms patients experienced and perceived as adverse following manual therapy and to explore predictors of adverse responses (AR) for the body region with the greatest number of AR. We hypothesized that patients receiving manual therapy for neck conditions would present with more symptoms perceived as AR.Methods: This was a secondary analysis of a previous cross-sectional survey of 324 patients receiving manual therapy from Canadian physiotherapists. It included questions regarding symptoms patients experienced after a treatment including manual therapy and perceived as adverse. Poisson and negative binomial regression were used to determine factors associated with the number of symptoms that patients experienced and perceived as adverse.Results: Symptoms that affected patient’s functionality were most often perceived as AR. The neck region was the body part with the greatest number of perceived AR (n = 83). Patients with neck pain who agreed that education may change their experience with AR had a lower incidence rate of AR.Conclusion: Findings indicate that communication regarding post-treatment symptoms between clinicians and patients is important and can potentially influence patients’ perception of post-treatment symptoms.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".