Consistency and Malleability of Manipulation Performance in Experienced Clinicians: A Pre-Post Experimental Design
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to sample the stability of spinal manipulation performance in peak impulse force development over time and the ability of clinicians to adapt to arbitrary target levels with short-duration training. METHODS: A pre-post experimental design was used. Human analog mannequins provided standardized simulation for performance measures. A convenience sample was recruited consisting of 41 local doctors of chiropractic with 5 years of active clinical practice experience. Thoracic impulse force was measured among clinicians at baseline, after 4 months at pretraining, and again posttraining. Intraclass correlation coefficient values and within-subject variability defined consistency. Malleability was measured by reduction of error (paired t tests) in achieving arbitrary targeted levels of force development normalized to the individual's typical performance. RESULTS: No difference was observed in subgroup vs baseline group characteristics. Good consistency was observed in force-time profiles (0.55 ≤ intraclass correlation coefficient ≤ 0.75) for force parameters over the 4-month interval. With short intervals of focused training, error rates in force delivery were reduced by 23% to 45%, depending on target. Within-subject variability was 1/3 to 1/2 that of between-subject variability. Load increases were directly related to rate of loading. CONCLUSION: The findings of this study show that recalibration of spinal manipulation performance of experienced clinicians toward arbitrary target values in the thoracic spine is feasible. This study found that experienced clinicians are internally consistent in performance of procedures under standardized conditions and that focused training may help clinicians learn to modulate procedure characteristics.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".