Multidisciplinary Approach to Prevent Limb Amputation in Diabetic Patients
Bibliographic record
Abstract
Diabetes mellitus is a complex disease with clinical sequelae including cardiovascular disease, renal failure, extremity complications, and death. Lower limb amputation is the most serious morbidity and consequence for diabetic patients due to its associated burden on quality of life and economic implications. Limb amputation is caused by a series of complications involving multiple organ systems. We believe that a multidisciplinary diabetic care team, consisting of vascular specialists, wound care specialists, and cardiologists, will facilitate in systematically addressing each complication leading to amputation. Diabetic patients have an increased risk of developing cardiovascular diseases, resulting in the expanding involvement and importance of interventional cardiologists on these multidisciplinary diabetic care teams. Several national and global institutions have successfully demonstrated the multidisciplinary approach to facilitate efficient and timely care, and most importantly, reduce the rates of diabetes-related limb amputation. The Heart, Vascular and Leg Center coordinates vascular specialty care and provides dedicated patient management in order to increase treatment quality, patient literacy, and medication adherence to decrease lower extremity amputation rates. J Endocrinol Metab. 2019;9(5):120-126 doi: https://doi.org/10.14740/jem615
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".