Posterolateral or direct lateral approach for hemiarthroplasty after femoral neck fractures: a systematic review
Bibliographic record
Abstract
BACKGROUND: The posterolateral approach (PLA) and direct lateral approach (DLA) are the most commonly used approaches for inserting a hemiarthroplasty in the treatment of femoral neck fractures. A recent review concluded that the routine use of PLA should be questioned, but this conclusion itself can be questioned. The aim of this study is to provide an updated overview and critical appraisal of the available evidence, focussing on outcomes most relevant for patients. METHODS: We conducted a comprehensive search of literature in the MEDLINE and EMBASE databases and Cochrane Library. Studies (till June 2018) to identify hip fracture clinical trials/comparative studies comparing alternative surgical approaches (PLA and DLA). We explored sources of heterogeneity and conducted pooled analyses when appropriate. RESULTS: 264 potentially eligible studies were identified of which 1 RCT, 3 prospective, 3 registry data and 5 retrospective studies were included. The RCT consisted performance and attrition bias. The mean MINORS score of the prospective/register studies was 17.3 (SD 3.5) and 13.8 (SD 1.9) of the 5 retrospective studies. The GRADE score for all the outcomes was very low. Due to the high and various types of biases across the included studies, we did not pool the data. None of studies assessed the activities of daily living functionality. 6 studies reported significantly more dislocations or reoperations due to dislocation in the PLA group, 6 other studies found no differences. DLA patients were more likely to develop abductor insufficiency leading to limping and more need for walking aids. The PLA patients tended to have better quality of life, less pain and more satisfaction compared to the DLA patients. CONCLUSION: Based on low-quality studies, PLA may be associated with more dislocations, but patients had less walking problems and a lower tendency to abductor insufficiency compared with DLA. Further clinical trials with methodology rigor are needed to determine which approach is more effective in terms of outcomes relevant to patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".