Performance and Reliability of a Variable Rate, Force/Displacement Application System
Bibliographic record
Abstract
OBJECTIVE: Spinal manipulation therapy (SMT), an intervention used to treat low back pain, has been demonstrated to affect the stiffness of the spine. To adequately quantify the effects of SMT on stiffness, a device capable of applying specific parameters of manipulation in addition to measuring force-displacement values has been developed previously. Previously developed indentation techniques that quantify stiffness have been modified for novel use in evaluating SMT parameters. The reliability of stiffness measurements performed by the newly adapted device was assessed in this study. METHODS: Seven springs of varying stiffness were each indented 10 times by a variable rate force/displacement (VRFD) device. Indentations were performed at a rate of 0.5 mm/s to a maximal displacement of 4 mm. The stiffness coefficients for a middle portion of the resulting force-displacement graph and the terminal instantaneous stiffness (stiffness at end displacement) were calculated. The intraclass correlation and confidence interval were calculated for these stiffness measurements to assess device reliability. RESULTS: Repeated spring stiffness measures yielded an intraclass correlation coefficient value of 1.0. The mean stiffness values had narrow 95% confidence intervals ranging from 0.01 N/mm to 0.06 N/mm and small coefficients of variation. CONCLUSION: This VRFD device provides highly reliable stiffness measurements in controlled conditions. Although in vivo reliability remains to be established, the results of this study support the use of the VRFD device in future trials investigating the impact of various SMT parameters on spinal stiffness.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".