Treatment of olecranon fractures in older individuals: a cross-sectional survey of surgeon treatment preferences
Bibliographic record
Abstract
Background: With an aging population, the incidence of olecranon fractures in older patients is increasing. The standard of care has traditionally included operative management for displaced fractures. Recent literature has called this standard of care into question. Older patients may be at increased risk of operative complications and may have satisfactory functional outcomes with nonoperative management. Given recently evolving evidence, the current treatment preferences of orthopedic surgeons for older patients with displaced olecranon fractures are unknown. Methods: We administered a cross-sectional survey of Canadian orthopedic surgeons via e-mail invitation and online survey form to determine treatment preferences for patients aged 65-75 and >75 years with simple displaced and comminuted displaced stable olecranon fractures. Respondents reviewed representative images and were asked to indicate their preferred treatment based on patient age. We also asked respondents to indicate their perceived importance of 11 patient factors on treatment decision-making. Results: = .004). For comminuted fractures, plate fixation was preferred for patients aged 65-75 years (n = 189, 95%) and >75 years (n = 131, 68%). In patients aged >75 years, this was followed by early range of motion (n = 35, 18%) and immobilization (n = 24, 13%). Of the 11 factors surveyed, participation in high-intensity activities (mean rank = 9.4), independent living (mean rank = 8.8), and disrupted extensor mechanism (mean rank = 8.3) were ranked most highly for increasing likelihood of surgical treatment. Conclusion: In patients aged 65 to 75 years, operative management is favored by most surgeons, with tension-band wiring preferred over plating for simple displaced fractures. In patients aged >75 years, operative management is again preferred by most respondents for simple and comminuted fractures. Despite operative preferences, there is a paucity of quality evidence to guide treatment decision-making, particularly in patients aged >75 years.
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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".