Trends in Digital Replantation: 10 Years of Experience at a Large Canadian Tertiary Care Center: Les tendances de la replantation digitale : dix ans d’expérience d’un grand centre canadien de soins tertiaires
Bibliographic record
Abstract
BACKGROUND: Since 1965, the practice of digital replantation has seen great technical strides and become commonplace worldwide. However, some American authors have recently reported declining rates of replantation. We set out to characterize the patient population and describe treatment patterns from 2005 to 2016 at a large Canadian regional replantation center. METHODS: A retrospective cohort of all patients undergoing digital replantation and revascularization from 2005 to 2016 was identified. Data were collected on demographics, injuries, procedures, and outcomes. Descriptive statistics were performed, followed by a comparison of two 5-year periods to evaluate temporal trends. RESULTS: A total of 234 patients were treated with 146 replantation and 204 revascularization procedures. Patients were largely male, healthy, and worked as manual labourers. Overall, the failure rate of individual repairs was 28.7%. Over time, there was a trend toward more crush or avulsion and multidigit injuries, and surgeries performed after 2011 were significantly longer. There was a significant downward trend in the number of patients treated at our center each year. Additionally, there was a statistically significant decrease in the proportion of replanted to revised digits in multidigit cases. DISCUSSION: Our observation of declining replantation rates is in line with recent American observations. The reason for this is not obvious but may represent a change in injury characteristics or surgeon attitudes. CONCLUSION: We suspect that these changes represent a change in workplace safety and injury characteristics, but further studies are needed to assess patient and surgeon treatment decisions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".