Cortical Strut Graft for Enigmatic Thigh Pain in Uncemented Total Hip Replacement
Bibliographic record
Abstract
Aims Enigmatic thigh pain in uncemented femoral components of a total hip replacement can be severe and disabling. Treatment can be conservative or surgical with cortical strut graft or revision of the femoral stem. Cortical strut grafting may offer good results with reduced morbidity. The aim of this study was to report the functional and radiographic outcomes of four patients with enigmatic thigh pain treated with cortical strut allograft. Materials and Methods Between 2016 and 2018, four women underwent cortical strut allografting at two centres. All patients had an uncemented, proximally porous S-ROM femoral implant (DePuy, Warsaw, In, USA). All other causes of anterolateral thigh pain were excluded. The mean age was 36.7 years (range: 29-51 years). Patients were followed up for a minimum of 14 months (range: 14-38 months). The University of California, Los Angles (UCLA) activity score, pain scores, complications, and radiographs at six weeks, three months, six months, nine months and one year were recorded. Results Mean UCLA activity scores increased from 3.2 (range: 2-4) to 6.2 (range: 6-7) post-operatively. Radiologically, all four patients had complete osseointegration of their strut grafts. Pain scores decreased at six weeks and at six months. One deep venous thrombosis occurred. One patient experienced recurrence of anterolateral thigh pain 26 months post-strut graft, which resolved with protected weight-bearing and analgesia for three months. Conclusions In uncemented femoral prostheses, cortical strut grafting to treat enigmatic thigh pain can reduce symptoms and increase activity without the need to revise a well-fixed femoral stem. We add to the growing body of evidence that this can be a successful surgical technique.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".