Consistency of strength curves for determining maximal effort production during isokinetic knee testing of anterior cruciate ligament-deficient patients
Bibliographic record
Abstract
The purpose of this investigation was to attempt to establish decision rules for determining maximal effort production during isokinetic strength testing of unilateral anterior cruciate ligament-deficient patients based on the degree of strength curve consistency within a set. Thirty-three participants performed six bilateral knee extension and flexion exertions at maximal effort and at 80% of perceived maximum at testing velocities of 60 and 180°s(-1). Within-set consistency was quantified by computation of the variance ratio across strength curves. Tolerance interval-based cutoff scores covering 99% of the population were calculated for declaring efforts as being maximal or not at confidence levels of 90%, 95%, and 99%. The sensitivity percentages attained for the injured knee for both testing velocities ranged between 9.1% and 27.2%, while specificity percentages ranged between 84.8% and 100%. For the non-injured knee, sensitivity values for both testing velocities ranged between 21.2% and 45.0%, while specificity percentages ranged between 97.0% and 100%. The developed decision rules do not effectively discriminate on an individual patient basis between maximal and non-maximal isokinetic knee musculature efforts. Further research is needed for development of methods that would enable to ascertain maximal effort production in this patient population during knee muscle strength testing.
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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 | 0.009 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".