Retracted: Application of Robot Positioning for Cannulated Screw Internal Fixation in the Treatment of Femoral Neck Fracture: Retrospective Study
Post-publication record
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Bibliographic record
Abstract
BACKGROUND: Femoral neck fracture is a common type of hip fracture. Conventional surgical treatment aims at fixing the fracture site with screws and then gradually promoting bone healing. A robot-assisted orthopedic surgery system is computer technology applied to surgical treatment. OBJECTIVE: This study aimed to explore the therapeutic effect and prognostic value of percutaneous cannulated screw internal fixation using robot-assisted positioning in patients with femoral neck fractures. METHODS: From July 2018 to September 2019, 42 cases of femoral neck fracture admitted to the Second Affiliated Hospital of Luohe Medical College were randomly and averagely divided into control and study groups. The patients in the control group were treated with conventional percutaneous cannulated screw internal fixation, while the patients in the study group were treated with robot-assisted percutaneous cannulated screw fixation during surgical treatment. We compared the treatment conditions and results of the operation between the 2 groups. The Harris score was used to evaluate the treatment efficacy. The state of fracture healing was followed up and compared between the 2 groups. RESULTS: The duration of the operation was shorter, there was less fluoroscopy use, and there were fewer drilled holes in the study group than in the control group (all, P<.001). There was no statistical difference in the amount of intraoperative bleeding between the 2 groups (P=.33). The Harris score (P=.045) and number of excellent and good ratings (P=.01) were significantly higher in the study group than in the control group. The difference in the fracture healing rate between the 2 groups was not statistically significant (P=.23). The fracture healing duration of the study group was shorter than that of the control group (P=.001). CONCLUSIONS: The use of robotic positioning aids in the treatment of femoral neck fractures with percutaneous cannulated screw fixation can effectively improve the efficiency of surgery, shorten the duration of surgery, and reduce the radiation damage to patients. Meanwhile, it improves postoperative treatment and recovery rates of the patients and shortens the fracture healing time.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".