A Systematic Review of Surgical Treatment for Refractory Patellar Tendinopathy
Bibliographic record
Abstract
INTRODUCTION: With the advent of new classification systems and surgical options, clarification is needed regarding the most effective surgical management for refractory patellar tendinopathy (PT). The objective of this systematic review is to investigate outcomes of surgical management of refractory PT. METHODS: PubMed was searched from January 1, 2000, through July 12, 2021, using the Medical Subject Headings function and the following Boolean operators: "Patellar Ligament/surgery"[Mesh] NOT "Anterior Cruciate Ligament"[Mesh]. Original research articles that discussed surgical management of PT were included, and outcomes were recorded. RESULTS: Fifteen studies (2 studies level 1 and 13 studies level 4) were included comprising 485 patients with an average age of 29.5 years and 523 patellar tendons treated. The seven studies reporting both preoperative and postoperative Victorian Institute of Sports Assessment scores demonstrated increases of 39.60, whereas the seven studies reporting visual analog scores demonstrated decreases of 6.11. Average return-to-play rate was 87.08%. Study designs and in surgical techniques were heterogenous, which precluded the ability to perform a meta-analysis. DISCUSSION: Surgical treatment of refractory PT leads to notable improvement in patient-reported outcomes and high return-to-play rates. However, there is a paucity of high-quality research investigating newer surgical options and classification systems for PT.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".