Predicting major adverse limb events in individuals with type 2 diabetes: Insights from the EXSCEL trial
Bibliographic record
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) (HRadj 4.83, 95% CI: 3.94–5.92), prior foot ulcer (HRadj 2.16, 95% CI: 1.63–2.87), prior amputation (HRadj 2.00, 95% CI: 1.53–2.64), current smoking (HRadj 2.00, 95% CI: 1.54–2.61), insulin use (HRadj 1.86, 95% CI: 1.52–2.27), coronary artery disease (HRadj 1.67, 95% CI: 1.38–2.03) and male sex (HRadj 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".