Comparing Quantification Methodologies for the Single-Leg Heel-Rise in Patients Following Operative and Non-Operative Management for Achilles Tendon Rupture
Bibliographic record
Abstract
Background: The Single-Leg Heel-Rise test (SLHR) is commonly used to determine the functional status of patients following Achilles tendon rupture (ATR). Several methods exist to quantify performance variables during the SLHR including repetition counting, linear position transducers (LPT) and motion capture/analysis systems; however, they have not been directly compared following operative or non-operative management of ATR. Hypothesis/Purpose: The purpose of this study was to compare three methods to quantify repetition number, heel-rise height, and total work during the SLHR test in patients managed either operatively or non-operatively for ATR. Study Design: Cross-sectional study. Methods: Twenty-four patients who underwent either operative or nonoperative treatment for complete ATR completed SLHR to failure on a 10° angle board. LPT, two-dimensional motion capture, and the Calf Raise smartphone application recorded heel-rise repetitions and height. Intraclass correlation coefficients and Bland-Altman plots (95% limits of agreement) compared devices. Work was reported as absolute values and limb symmetry index. Two-way analyses of variance were completed for all variables. Results: In this sample of 17 males and 7 females (operative [n=12; 39.0±8.9 years; 9 male] and non-operative [n=12; 46.2±14.1 years; 8 male] groups) strong, positive correlations were demonstrated between all devices, with the highest occurring between motion capture and the app (r2 =0.997). Bland-Altman Limits of Agreement showed wide limits of agreement across all methods. Peak and accumulated heel-rise height was reduced on the affected limb when measured by all devices, translating to reduced affected limb total work (mean difference: motion capture -403.5 Joules [J] (95%CI -41.5-902.6), app -434.7 J (95%CI -924.3-54.8), and LPT -370.4 J (95%CI -802.6-61.9). Conclusion: LPT, motion capture, and the calf raise app can be used to quantify repetitions, work, and heel-rise height in patients following ATR. However, device values should not be used interchangeably due to wide limits of agreement across methodologies. Level of Evidence: Level 2c.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".