Effectiveness of Platelet-rich Plasma Injection as an Adjunct Treatment to Arthroscopy for TFCC Injury: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: Triangular fibrocartilage complex (TFCC) injuries can cause significant patient dysfunction. Platelet-rich plasma (PRP) has emerged as a potential adjunctive treatment for arthroscopic TFCC repair, with some studies suggesting improved outcomes. This study aims to evaluate and compare PRP as an adjunctive treatment in arthroscopic TFCC tear repair. Methods: This retrospective cohort study examined patients undergoing arthroscopic TFCC repair at Institut de la Main, Paris, France (December 2021–2022). Patients were split into two groups: arthroscopic repair alone (1) and repair with PRP injections (2). Physical examinations were conducted pre- and posttreatment, recording flexion, extension, and radial/ulnar deviation of the affected and contralateral wrists. Results: A total of 33 patients (20 men and 13 women) with a mean age of 30.55 ± 9.17 years were included. PRP injections were given to 16 patients with arthroscopic TFCC repair; 17 had repair only. No significant differences existed preoperatively between groups in wrist function or pain (P > 0.05). The Quick Disabilities of the Arm, Shoulder, and Hand (DASH) score differed significantly (P = 0.004). The non-PRP group demonstrated better postoperative upper extremity function, with a mean Quick DASH score of 7.75 ± 5.91 compared with 12.64 ± 6.79 in the PRP group. No significant difference between groups was observed in the pain visual analog scale (P > 0.05). Conclusions: PRP injections with TFCC repair did not improve function over repair alone. The non-PRP group showed better function (lower Quick DASH scores). Pain reduction was similar between groups. Larger trials and cost-effectiveness studies are needed to fully assess PRP’s benefits in TFCC repair.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".