The forces applied by female and male chiropractors during thoracic spinal manipulation
Bibliographic record
Abstract
BACKGROUND: On average, women weigh less, have a smaller frame, and are less muscular than men. Since the peak thrust force applied during spinal manipulative treatments can be quite high and must be reached in a very short period of time, one might question the physical ability of women to generate such high forces. OBJECTIVE: To study the forces generated by male and female chiropractors as they deliver spinal manipulation to the thoracic spine. METHODS: Fourteen male and 14 female experience-matched chiropractors participated in this study. They each manipulated 1 of 9 asymptomatic male adult subjects of similar height and weight. The clinicians were asked to manipulate a transverse process in the vicinity of T4 and T9. Any technique could be used as long as the treatment thrust was in a posterior to anterior direction and the hand contact fit onto the sensor pad (area = 100 cm2). RESULTS: There were no significant differences (P<.05) between male and female chiropractors for any measurements in the upper thoracic area. For the lower thoracic manipulations, the preload forces for the male chiropractors were significantly greater (P<.05) than those for the female chiropractors. The remaining variables were the same between the 2 groups. CONCLUSIONS: Female chiropractors produce, from a mechanical point of view, similar manual treatments as their male colleagues.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".