IS OLDER AGE REALLY A PREDICTOR OF REOPERATIONS FOLLOWING PLATE FIXATION OF DISTAL RADIUS FRACTURES?
Bibliographic record
Abstract
There is continued debate about the best method of treatment in older patients with distal radius fractures (DRFs). We sought to characterize and identify predictors of reoperations following plate fixation of DRFs and to determine if older age was associated with worse outcomes. We used provincial administrative data to identify DRF patients aged 18 years and older between 2003-2016. We used procedural codes to ascertain those who underwent primary plate fixation within 30 days. We used procedural and diagnosis codes to examine reoperations related to the index fracture within 2 years of fixation. We used multivariable logistic regression to examine independent predictors of reoperations. We identified 13,165 DRF patients who underwent plate fixation between 2003 and 2016, 65.2% of whom were female, 25.9% aged 51-60 years, and 18.0% aged >61. Of these, 9.1% underwent a reoperation (mean 297.2±196.4 days). The most common reoperation was plate removal (7.0%, 309.8±183.7 days), followed by revision fixation (2.1% 252.6±216.8 days), and “other” reconstructive repair (1.8%, 352.2±188.7 days, includes osteotomy and capsular release). The most common diagnoses were hardware related concerns (5.4%), followed by non- or mal-union (1.4%), and median neuropathy (1.1%). We identified the following variables independently associated with an increased odds of reoperation: concomitant ulna fracture (odds ratio [OR] 1.25 [1.08-1.44]), time to fixation (OR 1.01 [1.00-1.02]), and female sex (OR 1.15 [1.01-1.32]). Specifically, concomitant ulnar fractures were associated with an increased odds of plate removal (OR 1.19 [1.02-1.40]) and revision fixation (OR 1.91 [1.48-2.47]); increased time to surgery was associated with a higher odds of revision fixation (OR 1.04 [1.02-1.06]); and female patients were more likely to undergo plate removal (OR 1.23 [1.06-1.43]), and nerve release or repair (OR 1.56 [1.01-2.44]). Further, we identified poor bone quality as an independent predictor of revision fixation (OR 1.82 [1.14-2.92]). Reoperations following plate fixation of DRFs appear to be driven primarily by hardware-related complications and tend to occur in those who experience a longer delay to surgery, females, and those with more complex fracture types or poor bone quality. While this suggests factors other than age cut-offs should be considered when deciding to perform plate fixation, further prospective research is required.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".