The Effect of Increasing Age on Outcomes of Digital Revascularization or Replantation
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to evaluate the impact of increasing age on rates of digital failure. METHOD: A retrospective cohort study of digital replantation or revascularization patients was undertaken from 2005 to 2016. Data collected consisted of patient demographics, smoking status, injury mechanisms, procedure types, and postoperative morbidity and mortality. Descriptive statistics and logistic regression were performed to assess outcomes. All comparisons were made between patients older than and younger than 60 years. RESULTS: Two hundred eighty-three patients underwent replantation or revascularization; 11 percent were older than 60 years. The majority of patients had multiple devascularized digits (70 percent), most commonly inflicted by a blade mechanism (77 percent). Approximately half of the patients underwent revascularization alone (54.4 percent). American Society of Anesthesiologists score and number of comorbidities were significantly greater in the older adult group. Overall, 88 patients (31 percent) experienced digital replantation or revascularization failure, with 12 failures in patients aged 60 years or older. Multivariate logistic regression demonstrated that age did not have an impact on failure rate. Older patients did not experience more major complications, but had significantly higher rates of minor complications (p = 0.0485). CONCLUSIONS: Older patients presented with significantly higher American Society of Anesthesiologists physical status and number of comorbidities, but did not experience higher rates of digital failure, major perioperative complications, or 30-day mortality. Adults aged 60 years or older should be offered digital replantation or revascularization if medically or surgically indicated. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, II.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".