Investigating force-time characteristics of prone thoracic SMT and self-reported patient outcome measures: a feasibility study
Bibliographic record
Abstract
BACKGROUND: Spinal manipulative therapy (SMT) is commonly used to treat musculoskeletal conditions, including thoracic spine pain. Applying patient-specific force-time characteristics are believed to be important to improve SMT's effectiveness. Investigating SMT as part of a multimodal approach is fundamental to account for the complexity of chiropractic clinical practice. Therefore, pragmatic investigations balancing minimal disruptions to the clinical encounter at the same time as ensuring a robust data quality with rigorous protocols are needed. Consequently, preliminary studies are required to assess the study protocol, quality of data recorded and the sustainability of such investigation. Therefore, this study examined the feasibility of investigating SMT force-time characteristics and clinical outcome measures in a clinical setting. METHODS: In this mixed-methods study, providers recorded thoracic SMT force-time characteristics delivered to patients with thoracic spinal pain during regular clinical encounters. Self-reported clinical outcomes of pain, stiffness, comfort during the SMT (using an electronic visual analogue scale), and global rating of change scale were measured before and after each SMT application. Feasibility was quantitatively assessed for participant recruitment, data collection and data quality. Qualitative data assessed participants' perceptions on the impact of data collection on patient management and clinical flow. RESULTS: Twelve providers (58% female, 27.3 ± 5.0 years old) and twelve patients (58% female, 37.2 ± 14.0 years old) participated in the study. Enrolment rate was greater than 40%, data collection rate was 49% and erroneous data was less than 5%. Participant acceptance was good with both providers and patients reporting positive experience with the study. CONCLUSIONS: Recording SMT force-time characteristics and self-reported clinical outcome measures during a clinical encounter may be feasible with specific modification to the current protocol. The study protocol did not negatively impact patient management. Specific strategies to optimize the data collection protocol for the development of a large clinical database are being developed.
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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 | 0.034 | 0.050 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".