Olecranon fractures
Bibliographic record
Abstract
Despite being one of the most common injuries around the elbow, the optimal treatment of olecranon fractures is far from established and stimulates debate among both general orthopaedic trauma surgeons and upper limb specialists. It is almost universally accepted that stable non-displaced fractures can be safely treated nonoperatively with minimal specialist input. Internal fixation is recommended for the vast majority of displaced fractures, with a range of techniques and implants to choose from. However, there is concern regarding the complication rates, largely related to symptomatic metalwork resulting in high rates of implant removal. As the number of elderly patients sustaining these injuries increases, we are becoming more aware of the issues associated with fixation in osteoporotic bone and the often fragile soft-tissue envelope in this group. Given this, there is evidence to support an increasing role for nonoperative management in this high-risk demographic group, even in those presenting with displaced and/or multifragmentary fracture patterns. This review summarizes the available literature to date, focusing predominantly on the management techniques and available implants for stable fractures of the olecranon. It also offers some insights into the potential avenues for future research, in the hope of addressing some of the pertinent questions that remain unanswered. Cite this article: Bone Joint J 2023;105-B(2):112–123.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".