Long-Term Outcomes Following Surgical Intervention for Achilles Tendon Rupture: A Systematic Review With a Minimum Five-Year Follow-Up
Bibliographic record
Abstract
Achilles tendon (TA) rupture is a common injury. However, there is limited evidence on the long-term outcomes following surgical repair. This systematic review aims to evaluate the efficacy and complications associated with surgical management of TA rupture with a minimum five-year follow-up duration. A literature search of Medline OVID, EMBASE, and Cochrane databases was performed adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The outcomes measured focused on postoperative complications, rates of re-rupture, contralateral rupture, and revision surgery. Six studies including 806 patients were included. Surgical repair demonstrated low re-rupture rates (4.2%) and revision rates (3.7%) at five years follow-up. Contralateral ruptures were uncommon (N=5), and 30 patients required revision surgery. However, complications such as deep vein thrombosis (6.3%) and wound infections (3.6%) were observed. Surgical repair of TA rupture demonstrates excellent long-term outcomes, including low rates of re-rupture and revision over a minimum of five years. While complications such as deep vein thrombosis and wound infections are observed, this systematic review highlights the reliability, durability, and overall success of surgical intervention in effectively treating this condition.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.003 | 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".