Comorbidity and risk factors of subsequent lower extremity amputation in patients diagnosed with diabetes in Saskatchewan, Canada
Bibliographic record
Abstract
OBJECTIVE: Subsequent limb amputation (SLA) may be necessary due to disease progression, infection, or to aid prosthesis fit. SLA in Saskatchewan has increased 3.2% from 2006 to 2019 with minor SLA increasing 9.6% during that period. Diabetes affects a large proportion of patients who require SLA; however, the impact of additional comorbidities is not clear. METHODS: First-episode subsequent lower extremity limb amputation (SLEA) cases with the presence/absence of diabetes, other comorbidities, and demographic characteristics from 2006-2019 were retrieved from Saskatchewan's Discharge Abstract Database. Logistic regression was performed to examine the magnitude of the odds of SLEA. RESULTS: Among the 956 first-episode SLEA patients investigated, 78.8% were diagnosed with diabetes. Of these, 76.1% were male and 83.0% were aged 50 + years. Three comorbidities: renal failure (AOR = 1.9, 95% Cl 1.1 - 3.0), hypertension (AOR = 3.0, 95% Cl 2.0 - 4.5), and congestive heart failure (AOR = 2.0, 95% CI 1.2 - 3.2), conferred the highest odds of SLEA. The odds of SLEA is greatest for those aged 50-69 years, males, Registered Indians, and associated with a prolonged hospital stay. DISCUSSION: These data are important as they may help medical providers identify patients at the highest risk of SLEA and target interventions to optimize outcomes.
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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 | 0.000 |
| 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".