A mini-anterior approach to the hip for total joint replacement: optimising results
Bibliographic record
Abstract
Direct anterior approaches to the hip have gained popularity as a minimally invasive method when performing primary total hip replacement (THR). A retrospective review of a single institution joint registry was performed in order to compare patient outcomes after THR using the Anterior Supine Intermuscular (ASI) approach versus a more conventional direct lateral approach. An electronic database identified 1511 patients treated with 1690 primary THRs between January 2006 and December 2010. Our results represent a summary of findings from our previously published work. We found that patients that underwent an ASI approach had faster functional recovery and higher Harris hip scores in the early post-operative period when compared with patients who had a direct lateral approach The overall complication rate in our ASI group was relatively low (1.7%) compared with other series using the same approach. The most frequent complication was early periprosthetic femoral fractures (0.9%). The dislocation rate in our series was 0.4% and the prosthetic joint infection rate was 0.1%. We suggest that the ASI approach is acceptable and safe when performing THR and encourages early functional recovery of our 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".