Posteroanterior motion test of a lumbar vertebra: accuracy of perception
Bibliographic record
Abstract
PURPOSE: The aim of this study was to investigate the accuracy of perception of forces applied to and displacement produced in an electromechanical one vertebral spinal model, among inexperienced and experienced physical, therapists performing posteroanterior pressure on a lumbar vertebra, before and after a training session. METHODS: Ten relatively inexperienced physical therapists and ten experienced manual therapists participated. An electromechanical single level spinal model was used for applying oscillatory posteroanterior pressure and measuring the forces on and displacement of the vertebra. A digital oscilloscope was used to give direct feedback to the therapists while performing mobilization to discern the magnitude of these two variables. RESULTS: The inexperienced group estimated the displacement accurately but the experienced group was significantly inaccurate (p<0.02), and both groups were inaccurate in estimating the force (p<0.001), before training. Following training, the inexperienced group maintained their accuracy on displacement and the experienced group improved their accuracy significantly (p<0.001). Both groups approached significance in improving their accuracy of force estimation. CONCLUSION: An electromechanical spinal model can be used as training tool along with an oscilloscope. Longer training may be needed for the force than the displacement for accurate perception.
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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 | 0.001 | 0.009 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".