Bibliographic record
Abstract
UNLABELLED: Massive bone defects are challenging problems in revision knee surgery. When defects are large and uncontained (without a cortical rim), structural allografts may be used to provide support for femoral and tibial components. This study reviewed 68 structural allografts at a mean of 5.4 years for clinical and radiographic outcomes. Indications for grafts included periprosthetic fracture in 19 knees, aseptic loosening in 29, infection in 11 and instability in 2. Seven knees had both femoral and tibial allografts. Multiple implant designs were used including 7 hinged prostheses. Thirteen knees (13/61) failed due to graft related complications including one graft nonunion, three aseptic loosenings, three periprosthetic fractures, four infections, and two for instability. The case of graft nonunion was successfully treated with revision fixation and autologous bone graft. There were three cases of graft resorption, two graded as severe and one as moderate. These results are satisfactory given the nature and complexity of the problem, however, reconstructive procedures require careful preoperative preparation and extensive experience in complex knee arthroplasty. LEVEL OF EVIDENCE: Therapeutic study, level IV (case series). See Guidelines for Authors for a complete description of levels of evidence.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".