Bibliographic record
Abstract
Diabetes is a serious chronic disease that results in foot complications for many people world-wide. In 2014, the World Health Organization estimated the global prevalence of diabetes in adults to be 9%. To ascertain the risk that an individual patient might develop a diabetic foot ulcer that could lead to an amputation, clinicians are strongly encouraged to perform a risk assessment. Monteiro-Soares and Dinis-Ribeiro have presented a new DIAbetic FOot Risk Assessment with the acronym DIAFORA. It is different from other risk assessments in that it predicts the risk of developing both diabetic foot ulcers and amputation specifically. The risk variables were derived by regression analysis based on a data set of 293 patients from a high-risk setting, a Hospital Diabetic Foot Clinic, who had diabetes and a diabetic foot ulcers. Clear descriptions of the risk variables are provided as well as sensitivity, specificity, positive and negative predictive values for the risk categories. As an added benefit, likelihood ratios are provided that will help clinicians determine the risk of amputation for individual patients. Having a risk assessment form is important for clinician use and examples exist. A question is raised about the effectiveness of risk assessment and how effectiveness might be determined.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".