What’s the harm? Results of an active surveillance adverse event reporting system for chiropractors and physiotherapists
Bibliographic record
Abstract
This prospective, community-based, active surveillance study aimed to report the incidence of moderate, severe, and serious adverse events (AEs) after chiropractic (n = 100) / physiotherapist (n = 50) visit in offices throughout North America between October-2015 and December-2017. Three content-validated questionnaires were used to collect AE information: two completed by the patient (pre-treatment [T0] and 2-7 days post-treatment [T2]) and one completed by the provider immediately post-treatment [T1]. Any new or worsened symptom was considered an AE and further classified as mild, moderate, severe or serious. From the 42 participating providers (31 chiropractors; 11 physiotherapists), 3819 patient visits had complete T0 and T1 assessments. The patients were on average 50±18 years of age and 62.5% females. Neck/back pain was the most common presenting condition (70.0%) with 24.3% of patients reporting no condition/preventative care. From the patients visits with a complete T2 assessment (n = 2136 patient visits, 55.9%), 21.3% reported an AE, of which: 7.9% were mild, 6.2% moderate, 3.7% severe, 1.5% serious, and 2.0% had missing severity rating. The most common symptoms reported with moderate or higher severity were discomfort/pain, stiffness, difficulty walking and headache. This study provides valuable information for patients and providers regarding incidence and severity of AEs following patient visits in multiple community-based professions. These findings can be used to inform patients of what AEs may occur and future research opportunities can focus on mitigating common AEs.
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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 | 0.026 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".