The effect of platelet-rich plasma on reducing blood loss after total knee arthroplasty
Bibliographic record
Abstract
BACKGROUND: Efficacy and safety of platelet-rich plasma (PRP) compared with control for preventing postoperative bleeding after total knee arthroplasty (TKA) is controversial. We performed a meta-analysis of randomized controlled trials (RCTs) to determine whether PRP might reduce blood loss and improve function following TKA. METHODS: PubMed, Medline, Embase, Web of Science, and the Cochrane Library were searched to identify RCTs comparing PRP with control for patients undergoing unilateral TKA. The mean difference (MD) of total blood loss, hemoglobin (Hb) level, Hb drop, drain volume, range of motion (ROM), Western Ontario and McMaster Osteoarthritis Index (WOMAC) scores, length of hospital stay (LOS), and odds ratios of transfusion rate and postoperative complications in the PRP and control groups were pooled throughout the study. Relevant data were meta-analyzed using RevMan v5.3. RESULTS: Six RCTs involving 529 patients were included (208 PRP vs. 321 controls). The application of PRP in TKA had a significantly less calculated total blood loss (MD = -98.11; 95% confidence interval [CI]: -153.63 to -42.59, P = .0005) and lower Hb drop (MD = -0.34; 95% CI: -0.59 to -0.09, P = .008) than the control in the early postoperative period while decreasing the LOS (MD = -2.12; 95% CI: -3.47 to -0.76, P = .002). No significant differences were seen in drain volume, Hb level, transfusion rate, ROM, WOMAC scores, and complications between the 2 groups (P > .05). CONCLUSIONS: Our meta-analysis suggests that PRP appears to be effective in reducing postoperative blood loss and lowering Hb drop without increasing the risks of postoperative complications after TKA. However, owing to the variation of included studies, no firm conclusions can be drawn.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.015 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".