Trunk Muscle Endurance in Individuals With and Without a History of Anterior Cruciate Ligament Reconstruction
Bibliographic record
Abstract
ABSTRACT: Werner, DM and Barrios, JA. Trunk muscle endurance in individuals with and without a history of anterior cruciate ligament reconstruction. J Strength Cond Res 35(1): 118-123, 2021-Anterior cruciate ligament (ACL) rupture is one of the most common knee injuries and often leads to surgery. Second injury after an ACL reconstruction (ACLR) is a major risk after rehabilitation, and may be linked to persistent postoperative deficits in muscular strength and endurance. Trunk muscle endurance has not been well studied after ACLR. Therefore, the purpose of this study was to compare trunk endurance using the established McGill testing battery in 20 individuals who had previously undergone ACLR at least 1 year before with 20 controls matched for sex frequency, limb dominance, age, body mass index, and activity level. Four static positional holds to failure were performed in random order, with time in seconds recorded as the primary dependent variable. Mann-Whitney U tests using an alpha level of 0.05 were conducted comparing hold times for all positions between groups. Effect sizes were also calculated between groups. Deficits in trunk extension endurance were observed in the surgical group. The results of this study suggest that contemporary rehabilitation schemes after ACLR do not fully address trunk endurance deficits. Health care professionals delivering postoperative rehabilitation after ACLR may consider direct assessment of trunk endurance and targeted exercise training to address potential deficits.
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 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".