Predictive factors for reporting adverse events following spinal manipulation in randomized clinical trials – secondary analysis of a systematic review
Bibliographic record
Abstract
While spinal manipulative therapy (SMT) is recommended for the treatment of spinal disorders, concerns exist about adverse events associated with the intervention. Adequate reporting of adverse events in clinical trials would allow for more accurate estimations of incidence statistics through meta-analysis. However, it is not currently known if there are factors influencing adverse events reporting following SMT in randomized clinical trials (RCTs). Thus our objective was to investigate predictive factors for the reporting of adverse events in published RCTs involving SMT. The Physiotherapy Evidence Database (PEDro) and Cochrane Central Register of Controlled Trials (CENTRAL) were searched for RCTs involving SMT. Domains of interest included: sample size; publication date relative to the 2010 CONSORT statement; risk of bias; the region treated; and number of intervention sessions. 7398 records were identified, of which 368 articles were eligible for inclusion. A total of 140 (38.0%) articles reported on adverse events. Articles were more likely to report on adverse events if they possessed larger sample sizes, were published after the 2010 CONSORT statement, had a low risk of bias and involved multiple intervention sessions. The region treated was not a significant predictor for reporting on adverse events. Predictors for reporting on adverse events included larger sample size, publication after the 2010 CONSORT statement, low risk of bias and trials involving multiple intervention sessions. We recommend that researchers focus on developing robust methodologies and participant follow-up regimens for RCTs involving SMT.
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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 | 0.058 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".