Pressures Generated During Spinal Manipulation and Their Association With Hand Anatomy
Bibliographic record
Abstract
BACKGROUND CONTEXT: The role of the variation in the application manipulation itself is largely unknown. A greater understanding of its input parameters is necessary to better understand spinal manipulation outcomes. PURPOSE: The objective of this study is to determine if pressures generated during manipulation are altered by hand configuration. DESIGN/SETTING: Paired comparison of 2 different variable groups. METHODS: Sixteen chiropractors provided 2 manipulations to a rigid surface using 2 hand configurations used commonly in clinical practice: arched and flat. Interposed between the hand and the rigid surface was a pressure sensor array and radiographic cassette. For each manipulation, pressures were recorded and a radiographic image was captured. Two radiologists then located the osseous features of the hand with respect to the sensor array. RESULTS: In 15 of 16 cases, arched configurations produced peak pressures that corresponded to the radiographic location of the pisiform bone. In flat configurations, peak pressure migrated about the location of the hamate bone. Radiologists' agreement for bone position was high (kappa = 0.96). Measures of peak pressure, total pressure, and pressure distribution were statistically different between hand configurations. CONCLUSIONS: The results of this study suggest that hand configuration influences the magnitude, location, and distribution of pressure generated by the hand during manipulation. This knowledge may have importance in understanding the relation among application parameters of manipulation, therapeutic benefit, and patient safety.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".