Ascertaining Maximal Voluntary Effort Production During Isokinetic Knee Strength Testing of Anterior Cruciate Ligament–Reconstructed Patients
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to assess the performance of prediction rules meant for declaration of efforts as being maximal or not during isokinetic strength testing in a cohort that underwent anterior cruciate ligament reconstruction. DESIGN: Thirty-six individuals performed four sets of six reciprocal concentric knee extension/flexion repetitions at a testing speed of 60 degrees per second through a 60-degree range of motion. The sets consisted of a maximal voluntary effort, two nonmaximal sincere efforts at 50% and 75% of self-perceived maximum, and a set attempting to feign or exaggerate thigh muscle strength deficiencies. Strength curve derived set internal consistency measures, namely, cross-correlation and percent root mean square difference scores, were inputted into the prediction rules, whose performance is reported as specificity and sensitivity percentages. RESULTS: Dependent on the prediction rule used and when expressed on an individual participant basis, the corresponding specificity and sensitivity values ranged from 66.6% to 97.2% and 97.2% to 94.4%, respectively. CONCLUSIONS: Using the prediction rules presented in this investigation, clinicians may be able to ascertain maximal effort production during isokinetic testing in those who have undergone surgical reconstruction of their anterior cruciate ligament.
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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 | 0.002 | 0.013 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".