Defining and classifying adverse events following joint manipulation and mobilization: An international e-Delphi study and focus groups
Bibliographic record
Abstract
Spinal and peripheral joint manipulation (MAN) and mobilization (MOB) are widely used for managing musculoskeletal conditions. Although adverse events (AE) have been reported following these interventions, there is no universally accepted definition and classification system. This study aimed to establish an inter-professional and international standardized definition and severity classification for AE following MAN and MOB. This sequential mixed-methods study included an electronic Delphi process (e-Delphi) followed by focus groups. Inter-professional and international expert stakeholders participated in 3 e-Delphi rounds: Round 1 included open-ended questions on participants' working AE definition and severity classification; Round 2, level of agreement with statements generated from Round 1 and a previous scoping review; and Round 3, level of agreement with statements achieving consensus in Round 2. Focus groups explored e-Delphi findings. Consensus was reached for severity categories (i.e., mild, moderate, severe and catastrophic) and on 2 domains to differentiate these categories (i.e., symptom intensity and impact on patient). Consensus was not reached for a standardized AE definition following MAN and MOB. Focus group discussions centered on "unfavourable", "unexpected" and "undesired" terms and differences between "serious" and "catastrophic" severity classification categories. Findings contribute to advancing patient safety and AE knowledge across professions and informing further safety research and practice.
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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 | 0.127 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".