Predicting major adverse limb events in individuals with type 2 diabetes: Insights from the EXSCEL trial
Bibliographic record
Abstract
Abstract Aims Although models exist to predict amputation among people with type 2 diabetes with foot ulceration or infection, we aimed to develop a prediction model for a broader range of major adverse limb events (MALE)—including gangrene, revascularization and amputation—among individuals with type 2 diabetes. Methods In a post‐hoc analysis of data from the Exenatide Study of Cardiovascular Event Lowering (EXSCEL) trial, we compared participants who experienced MALE with those who did not. A multivariable model was constructed and translated into a risk score. Results Among the 14,752 participants with type 2 diabetes in EXSCEL, 3.6% experienced MALE. Characteristics associated with increased risk of MALE were peripheral artery disease (PAD) (HR adj 4.83, 95% CI: 3.94–5.92), prior foot ulcer (HR adj 2.16, 95% CI: 1.63–2.87), prior amputation (HR adj 2.00, 95% CI: 1.53–2.64), current smoking (HR adj 2.00, 95% CI: 1.54–2.61), insulin use (HR adj 1.86, 95% CI: 1.52–2.27), coronary artery disease (HR adj 1.67, 95% CI: 1.38–2.03) and male sex (HR adj 1.64, 95% CI: 1.31–2.06). Cerebrovascular disease, former smoking, age, glycated haemoglobin, race and neuropathy were also associated significantly with MALE after adjustment. A risk score ranging from 6 to 96 points was constructed, with a C‐statistic of 0.822 (95% CI: 0.803–0.841). Conclusions The majority of MALE occurred among participants with PAD, but participants without a history of PAD also experienced MALE. A risk score with good performance was generated. Although it requires validation in an external dataset, this risk score may be valuable in identifying patients requiring more intensive care and closer follow‐up.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".