Comparison of active versus passive surveillance adverse event reporting in a paediatric ambulatory chiropractic care setting: a cluster randomised controlled trial
Bibliographic record
Abstract
OBJECTIVES: This pragmatic, cluster, stratified randomised controlled trial (RCT) compared the quantity and quality of adverse event (AE) reports after chiropractic manual therapy in children less than 14 years of age, using active versus passive surveillance reporting systems. METHOD: Data were collected between November 2014 and July 2017 from 60 consecutive paediatric patient visits to participating chiropractors. Those allocated to active surveillance collected AE information with three paper-based questionnaires (two from patients, one from chiropractors) to identify any new or worsening symptoms after treatment. Passive surveillance involved AE information reported by chiropractors on a web-based system. To assess quality of reporting, AE reports greater than mild were reviewed by content experts. The primary outcome was the cumulative incidence of AE reports in active versus passive surveillance. RESULTS: Ninety-six chiropractors agreed to participate and enrolled in the study: 34 chiropractors in active surveillance with 1894 patient visits from 1179 unique patients and 35 chiropractors in passive surveillance with 1992 patient visits from 1363 unique patients. In the active arm, AEs were reported in 8.8% (n=140, 95% CI 6.72% to 11.18%) of patients/caregivers, compared with 0.1% (n=2, 95% CI 0.02% to 0.53%) in the passive arm (p<0.001). The quality of AE reports was not evaluated because the five AE reports reviewed by the content experts were determined to be of mild severity. CONCLUSION: We found that active surveillance resulted in significantly more AE reports than passive surveillance. Further prospective active surveillance research studies should be conducted with children receiving chiropractic manual therapy to understand mechanisms and risk factors for moderate and severe AEs, and to further explore how and when to solicit patient safety information.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".