Beliefs, perceptions and practices of chiropractors and patients about mitigation strategies for benign adverse events after spinal manipulation therapy
Bibliographic record
Abstract
BACKGROUND: Approximately 50% of patients who receive spinal manipulative therapy (SMT) experience some kind of adverse event (AE), typically benign and transient in nature. Regardless of their severity, mitigating benign AEs is important to improve patient experience and quality of care. The aim of this study was to identify beliefs, perceptions and practices of chiropractors and patients regarding benign AEs post-SMT and potential strategies to mitigate them. METHODS: Clinicians and patients from two chiropractic teaching clinics were invited to respond to an 11-question survey exploring their beliefs, perceptions and practices regarding benign AEs post-SMT and strategies to mitigate them. Responses were analyzed using descriptive statistics. RESULTS: A total of 39 clinicians (67% response rate) and 203 patients (82.9% response rate) completed the survey. Most clinicians (97%) believed benign AEs occur, and 82% reported their own patients have experienced one. For patients, 55% reported experiencing benign AEs post-SMT, with the most common symptoms being pain/soreness, headache and stiffness. While most clinicians (61.5%) reported trying a mitigation strategy with their patients, only 21.2% of patients perceived their clinicians had tried any mitigation strategy. Clinicians perceived that patient education is most likely to mitigate benign AEs, followed by soft tissue therapy and/or icing after SMT. Patients perceived stretching was most likely to mitigate benign AEs, followed by education and/or massage. CONCLUSIONS: This is the first study comparing beliefs, perceptions and practices from clinicians and patients regarding benign AEs post-SMT and strategies to mitigate them. This study provides an important step towards identifying the best strategies to improve patient safety and improve quality of care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".