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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".