Periprosthetic Postoperative Humeral Fractures After Shoulder Arthroplasty
Bibliographic record
Abstract
The increased utilization of shoulder arthroplasty, including revision procedures, combined with rises in life expectancy, is expected to translate into a substantial increase in periprosthetic humeral fractures. The evaluation and management of these fractures needs to be updated to consider fractures that complicate anatomic and reverse arthroplasties and contemporary short-stem and stemless implants. Although conservative treatment is successful in a large proportion of these fractures, several surgical reconstructive techniques are required for the management of all fracture types. Surgical options include internal fixation, graft augmentation, standard revision procedures, and occasionally complex reconstructions including modular segmental prosthesis and allograft prosthetic composites. Most studies on the outcomes of periprosthetic humeral fractures have analyzed small samples and have typically reported on anatomic total shoulders with a standard-length humeral implant. Additional research is required to optimize the management of periprosthetic postoperative humeral fractures in the era of reverse arthroplasty, short stems, and stemless arthroplasty.
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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".