Use of the supercapsular percutaneously assisted total hip approach for femoral neck fractures: surgical technique and case series
Bibliographic record
Abstract
BACKGROUND: Femoral neck fractures are common injuries in the geriatric population associated with high morbidity and mortality rates. Studies have shown outcomes can be positively influenced by early postoperative mobilization. The supercapsular percutaneously assisted total hip (SuperPath) surgical technique has been shown to lead to early mobilization for osteoarthritic total hip replacement patients and as such has the potential to provide similar benefits in fracture patients. This manuscript provides a detailed description of this technique using hemiarthroplasty to treat femoral neck fractures and presents the first case series of this application. METHODS: Seventeen patients with femoral neck fractures managed with this technique at two separate institutions were reviewed. In an attempt to minimize blood loss and enhance early mobilization, hemiarthroplasty utilizing the SuperPath technique was performed. The authors noticed decreased blood loss, operative time, and postoperative narcotic usage when compared to their previous experiences using traditional techniques. CONCLUSIONS: Early mobilization following femoral neck fractures has been shown to decrease mortality and morbidity. There is little existing literature on the use of tissue-sparing surgical techniques for this application, and none details the use of the SuperPath technique for it. The described case reports suggest the technique is a viable option for bipolar hemiarthroplasty to treat femoral neck fractures. Appropriately designed future studies are needed to confirm findings and definitively compare outcomes to traditional approaches.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".