Weight Loss in Patients Waiting for Total Hip Arthroplasty: Fiber-Enriched High Carbohydrate Diet Improves Hip Function and Decreases Pain before Surgery
Bibliographic record
Abstract
The impact of obesity on clinical outcomes following joint replacement procedures is resounding. Therefore, multiple strategies to achieve a substantial weight loss before surgery are needed in obese patients. The aim of the study was to test the effect of a fiber-enriched high carbohydrate (FEHC) diet on the reduction in body weight and pain in elderly obese patients undergoing total hip arthroplasty (THA). Sixty-one candidates for THA were included in our study. Prior to the procedure, the participants have been randomly assigned to a 3-month diet intervention (FEHC diet or free diet). Anthropometric measures and food questionnaires were collected at the enrollment and after 3 months. The Oxford Hip Score (OHS), the Hip disability and Osteoarthritis Outcome Score (HOOS) and the Western Ontario McMaster Universities OA Index (WOMAC) were administered at baseline and before surgery. A statistically significant variation of weight was found in the FEHC diet group (−3.7 kg, −4.4–−2.5) compared to the control group (−0.2 kg; −1.4–1.7; p < 0.0001), as well as significant improvements in the OHS (p < 0.0001), the HOOS (p < 0.0001) and the WOMAC (p < 0.0001) questionnaires. According to the results of the study, the FEHC diet in obese patients undergoing THA might help weight loss and improve related anthropometric parameters as well as hip function and pain.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".