Assessing adverse events associated with chiropractic care in preschool pediatric population: a feasibility study
Bibliographic record
Abstract
BACKGROUND: Manual therapies are commonly used by healthcare professionals when caring for children. However, few prospective studies have evaluated their adverse events (AEs). This study aims to assess the feasibility of a pragmatic prospective study aiming to report the immediate and delayed (48-hours post-treatment) AEs associated with manual therapies in children aged 5 or younger. Preliminary data on AEs frequency are also reported. METHODS: Between July 2021 and March 2022, chiropractors were recruited through purposive sampling and via a dedicated Facebook group for Quebec chiropractors interested in pediatrics. Legal guardians of patients aged 5 or younger were invited to fill out an online information and consent form. AEs were collected using the SafetyNET reporting system, which had been previously translated by the research team. Immediate AEs were collected through a questionnaire filled out by the legal guardian immediately after the treatment, while delayed AEs were collected through a questionnaire sent by email to the legal guardian 48 h after the treatment. Feasibility was assessed qualitatively through feedback from chiropractors and quantitatively through recruitment data. RESULTS: Overall, a total of 28 chiropractors expressed interest following the Facebook publication, and 5 participated. An additional two chiropractors were enrolled through purposive sampling. In total, 80 legal guardians consented to their child's participation, and data from 73 children were included for the analysis of AEs. At least one AE was reported in 30% of children (22/73), and AEs were mainly observed immediately following the treatment (16/22). The most common AEs were irritability/crying (11 children) or fatigue/tiredness (11 children). Feasibility analysis demonstrated that regular communication between the research team and clinicians, as well as targeting clinicians who showed great interest in pediatrics, were key factors for successful research. CONCLUSION: Results suggest that it is feasible to conduct a prospective pragmatic study evaluating AEs associated with manual therapies in private practices. Direct communication with the clinicians, a strategic clinicians' recruitment plan, and the resulting administrative burden should be considered in future studies. A larger study is required to confirm the frequency of AEs reported in the current study. TRIAL REGISTRATION: ClinicalTrials.gov., NCT05409859, Registered on June 3 2022. https://clinicaltrials.gov/study/NCT05409859 .
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".