Retracted: Application of Intelligent Computer-Assisted Taylor 3D External Fixation in the Treatment of Tibiofibular Fracture: Retrospective Case Study
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
BACKGROUND: With the development of modern society, severe and complex tibial fractures caused by high-energy injuries such as traffic accidents have gradually increased. At present, the commonly used methods for the treatment of tibial fractures include plate fixation, intramedullary nail fixation, and external fixation. Most of these fractures are open wounds with severe soft tissue injury and wound contamination, and some involve bone defects, which makes internal fixation treatment difficult. OBJECTIVE: This study aims to explore the use of intelligent computer-assisted Taylor 3D external fixation for the treatment of tibiofibular fractures. METHODS: In total, 70 patients were included and divided into the Taylor 3D external fixation (TSF) group (28 patients with severe tibial fractures treated with TSF) and the internal fixation group (42 patients with complicated tibiofibular fractures treated by internal fixation). After the treatment, the follow-up evaluation of TSF for the treatment of tibiofibular fractures noted the incidence of complications, as well as the efficacy and occurrence of internal fixation for the treatment of tibial fractures in our hospital. RESULTS: The results showed that TSF was superior to orthopedics in the treatment of tibiofibular fractures in terms of efficacy and complications. CONCLUSIONS: TSF for the treatment of tibiofibular fractures is more effective than internal fixation and the incidence of complications is low. This is a new technology for the treatment of tibiofibular fractures that is worthy of clinical promotion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".