Guided imagery to improve functional outcomes post‐anterior cruciate ligament repair: randomized‐controlled pilot trial
Bibliographic record
Abstract
Imagery can improve functional outcomes post-anterior cruciate ligament repair (ACLR). Research is needed to investigate potential mechanisms for this effect. The aim of this study was to (a) evaluate the effectiveness of an imagery intervention to improve functional outcomes post-ACLR, and (b) explore potential mechanisms. A randomized-controlled pilot trial was conducted. Participants were randomized to guided imagery and standard rehabilitation or standard rehabilitation alone (control). The primary outcome was knee strength 6-month post-operatively. Secondary outcomes were knee laxity at 6-months, and change in psychological (self-efficacy) and neurohormonal (adrenaline, noradrenaline, dopamine) variables. Participants (n=21; 62% male) were 34.86 (SD 8.84) years. Following the intervention, no statistical differences between groups for knee strength extension at 180°/s (t=-0.43, P=0.67), or at 60°/s (t=-0.72, P=0.48) were found. A statistically significant effect was found for knee laxity, F=4.67, P<0.05, mean difference of -3.02 (95% CI -4.44 to -1.60), favoring the intervention. No differences were found for self-efficacy; however, an overall effect was found for noradrenaline, F(1, 19) 19.65, P<0.001, η(2) =0.52, and dopamine, F(1, 19) 6.23, P=0.02, η(2) =0.29, favoring the intervention. This imagery intervention improved knee laxity and healing-related neurobiological factors.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".