Variability of Force Magnitude and Force Duration in Manual and Instrument-Based Manipulation Techniques
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to compare the variation of manipulative forces produced by instruments and a manual technique. METHODS: Four operators (2 experts and 2 novices) used 4 different mechanical instruments to apply force to a uniaxial load cell. A different group of 2 expert and 2 novice operators used a traditional manual technique to apply force to a sensor mat. Two primary outcome variables were obtained from each sensor system: peak-to-peak force magnitude (N) and peak-to-peak force duration (millisecond). Multiple analyses were performed to determine the absolute differences and variation in each variable. RESULTS: Force-producing instrumentation exhibited less variation in absolute force and force duration compared to manual techniques. However, the same instrument in the hands of different operators often produced significantly different values of absolute force and force duration. Although absolute values of force magnitude generally differed between operators, intraoperator variation was equal for instruments and the manual technique. Conversely, for force duration, significant differences in interoperator variability were observed for the manual technique and for one of the instruments. CONCLUSIONS: Force-producing instruments reduce absolute variation in force magnitude and duration. However, this reduction does not eliminate significant differences in absolute force parameters observed to occur between some operators using the same instrument. Given these observations, claims of instrument superiority that do not account for interoperator variability should be considered with caution.
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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 | 0.003 | 0.023 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".