What Is the Role of Nutritional Supplements in Support of Total Hip Replacement and Total Knee Replacement Surgeries? A Systematic Review
Bibliographic record
Abstract
Nutritional supplements can influence outcomes for individuals undergoing major surgery, particularly in older persons whose functional reserve is limited. Accelerating recovery from total hip replacement (THR) and total knee replacement (TKR) may offer significant benefits. Therefore, we explored the role of nutritional supplements in improving recovery following THR and TKR. A systematic review was conducted to source randomized clinical trials that tested nutritional supplements in cohorts of THR or TKR patients. Our search yielded nine relevant trials. Intake of a carbohydrate-containing fluid is reported to improve insulin-like growth factor levels, reduce hunger, nausea, and length of stay, and attenuate the decrease in whole-body insulin sensitivity and endogenous glucose release. Amino acid supplementation is reported to reduce muscle atrophy and accelerate return of functional mobility. One paper reported a suppressive effect of beta-hydroxy beta-methylbutyrate, L-arginine, and L-glutamine supplementation on muscle strength loss following TKR. There is limited evidence for nutritional supplementation in THR and TKR pathways; however, the low risk profile and potential benefits to adjunctive treatment methods, such as exercise programs, suggest nutritional supplements may have a role. Optimizing nutritional status pre-operatively may help manage the surgical stress response, with a particular benefit for undernourished, frail, or elderly individuals.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".