Differing Characteristics of Human-Shaped Visual Stimuli Affect Clinicians’ Dosage of a Spinal Manipulative Thrust on a Low-Fidelity Model: A Cross-Sectional Study
Bibliographic record
Abstract
Objective The purpose of this study was to determine whether chiropractic clinicians modulate spinal manipulation (SM) thrust characteristics based on visual perception of simulated human silhouette attributes. Methods We performed a cross-sectional within-participant design with 8 experienced chiropractors. During each trial, participants observed a human-shaped life-sized silhouette of a mock patient and delivered an SM thrust on a low-fidelity thoracic spine model based on their visual perception. Silhouettes varied on the following 3 factors: apparent sex (male or female silhouette), height (short, average, tall), and body mass index (BMI) (underweight, healthy, obese). Each combination was presented 6 times for a total of 108 trials in random order. Outcome measures included peak thrust force, thrust duration, peak preload force, peak acceleration, time to peak acceleration, and rate of force application. A 3-way repeated measures analysis of variance model was used to for each variable, followed by Tukey's honestly significant difference on significant interactions. Results Peak thrust force was reduced when apparent sex of the presented silhouette was female (F 1,7 = 5.70, P = .048). Thrust duration was largely invariant, except that a BMI by height interaction revealed a longer duration occurred for healthy tall participants than healthy short participants (F 4,28 = 4.34, P = .007). Compared to an image depicting obese BMI, an image appearing underweight lead to reduced peak acceleration (F 2,5 = 6.756, P = .009). Clinician time to peak acceleration was reduced in short compared to tall silhouettes (t 7 = 2.20, P = .032). Conclusion Visual perception of simulated human silhouette attributes, including apparent sex, height, and BMI, influenced SM dose characteristics through both kinetic and kinematic measures. The results suggest that visual information from mock patients affects the decision-making of chiropractic clinicians delivering SM thrusts.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".