Torque: An appraisal of misuse of terminology in chiropracticliterature and technique
Bibliographic record
Abstract
Harrison et al 1 Harrison DD Colloca CJ Troyanovich SJ Harrison DE. Torque: an appraisal of misuse of terminology in chiropractic literature and technique. J Manipulative Physiol Ther. 1996; 19: 454-462 PubMed Google Scholar , 2 Harrison DD Colloca CJ Troyanovich SJ Harrison DE. Torque misuse revisited. J Manipulative Physiol Ther. 1998; 21: 649-655 PubMed Google Scholar , 3 Harrison DD Troyanovich SJ Harrison DE. Commentary: torque misuse revisited. J Manipulative Physiol Ther. 1999; 22: 347-348 Abstract Full Text Full Text PDF PubMed Google Scholar have written a series of papers on the mechanics of spinal manipulation; I have shown that the mechanics they used were incorrect. 4 Herzog W. Torque: misuse of a misused term. J Manipulative Physiol Ther. 1998; 21: 57-59 PubMed Google Scholar They accepted my argument in some cases but not in others. These other cases involved some of the most important and basic principles of Newtonian mechanics. Because their disagreement was emotional rather than scientific, the person not trained in mechanics may be at a loss about who is right and who is wrong.
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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 | 0.038 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.029 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".