The direct anterior approach to the hip for total hip arthroplasty: a blind guide (with traction table)
Bibliographic record
Abstract
The direct anterior approach (DAA) to the hip is gaining popularity worldwide. It has even become an integral part of orthopaedic training programs across the globe. This approach is well known for its long learning curve, which makes it challenging for residents and fellows to master in a short period of time during their rotations to different subspecialties. There is good evidence to support utilising this approach for a total hip arthroplasty (THA) as it affords patients an improvement in early recovery by way of better gait and kinematics compared to traditional approaches. This approach can be used to expedite patient’s recovery with the aim of an early discharge in the form of an outpatient THA. The enhanced recovery program and day case hip arthroplasty using this approach is our standard practice and works perfectly with this muscle sparing approach. The aim of this article is to present a step-by-step guide for this approach for residents and fellows, and can be adopted by any surgeon working in a teaching setting. This is a full description of our institutional anterior approach to the hip that can be used for primary THA as well as revision cases using a dedicated traction table. This approach can be used for treating femoral neck fractures, periprosthetic infections and periprosthetic fractures. Femoral osteotomy for revision cases can be utilised in a similar fashion to other traditional approaches.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.014 |
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".