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Record W4415439294 · doi:10.1302/1358-992x.2025.10.084

COMPARING QUANTIFICATION METHODOLOGIES FOR THE SINGLE LEG-HEEL RISE TEST IN PATIENTS FOLLOWING OPERATIVE AND NONOPERATIVE MANAGEMENT FOR ACHILLES TENDON RUPTURE

2025· article· en· W4415439294 on OpenAlexaff
Robert Longstaffe, John Garofalo, S. Zhu, Monther Abuhantash, Sheila McRae, P.B. MacDonald, Daniel I. Ogborn, Jarret M. Woodmass

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsPan Am Clinic
Fundersnot available
KeywordsAchilles tendon ruptureAchilles tendonHeelSagittal planeCalcaneusLower limbProne positionTendon

Abstract

fetched live from OpenAlex

The Single Leg Heel Rise (SLHR) is commonly used to determine the functional status of patients following Achilles Tendon Rupture (ATR). The number of repetitions per limb is often not sufficient to quantify performance deficits, whereas peak heel rise height and total work may be more sensitive. Multiple methods exist to quantify performance variables during the SLHR including linear position transducers, video motion capture and mobile video technologies; however, these methodologies have not been directly compared in their quantification of SLHR performance following operative or non-operative management of Achilles Tendon rupture. Patients were retrospectively identified that were between the ages of 18–65, who had a complete ATR that was treated within three weeks of injury with either operative or non-operative management. The SLHR was completed to exhaustion with maximal, unilateral heel rises on a 10oangle board. A linear position transducer (LPT; GymAware, ACT, AUS) was anchored to the posterior calcaneus during the test. One sagittal plane camera (Ninox 125, Noraxon, AZ, USA) recorded for both the motion analysis data, which was post-processed in MyoVideo (MoCap; Noraxon, AZ, USA) and the Calf Raise App (APP; Hebert-Losier, 2020). Between limb and group differences in all variables were compared with a two-way ANOVA. Pearson correlations and Bland Altman plots (limits of agreement (LOA)) were completed on work and limb symmetry index (LSI) values. Twenty four patients (n=12 operative and non-operatively treated; 42.6 ± 12.3 years, 174.9 ± 8.0 cm, 92 ± 18.8 kg, 17 male, 7 female) completed the SLHR. Total SLHR repetitions were lower in the non-operative group when measured with MoCap and the APP (p<0.05) but not the LPT. Peak heel rise height was reduced on the affected limb regardless of group across all three methods (p<0.05). Total heel rise height over the test was greater in the operative group regardless of limb when measured with MoCap and APP (p<0.05) but not the LPT. While total work was reduced on the affected limb, and more so in the non-operative group, this did not reach the threshold for statistical significance. Strong, positive correlations were demonstrated between all devices for total work, with the highest between MoCap and the APP (r2= 0.997), followed by the LPT and APP (r2= 0.906) and MoCap and LPT (r2= 0.905; p<0.001 for all). Similarly strong correlations were demonstrated (r2= 0.781 – 0.990) across devices for work LSI. Agreement for LSI was acceptable between MoCap and App (−0.067 % (95%LOA −6.38–6.25%); However, limits of agreement were broader for total work across all devices, or between MoCap or APP and the LPT for LSI. The Calf Raise App represents a simple method for both patients and clinicians to quantify between-limb deficits following operative or non-operative management of ATR during the SLHR using readily available technology. While work and LSI values between video capture technologies agree, they are not interchangeable with those from LPTs due to wide limits of agreement despite strong correlations amongst all devices.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.044
GPT teacher head0.333
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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