606-P: Artificial Neuronal Network Model for Three-Month Prognosis in Diabetic Foot Syndrome
Bibliographic record
Abstract
Background: To make a reliable prognosis on wound healing in patients with diabetic foot syndrome (DFS) is extremely difficult. We used the artificial neuronal network (ANN) to identify the most significant variables which affect DFS healing process. We also aimed at providing data for designing a digital application which would help practitioners to predict the course of DFS. Methods: We enrolled 213 DFS patients and examined them using a variety of diagnostic tests. The patients were followed for three months. In the initial model, we assessed 35 clinical and biochemical variables. Subsequently, with the help of traditional statistics we reduced their number to twelve, and after conducting the sensitivity analysis, we found out that the final number of significant variables is as low as six. Results: The most significant variables in predicting the outcome of DFS treatment were: probe-to-bone test result, presence of blood flow in Doppler probe, prior amputation within the foot, erythrocyte sedimentation rate, and the area and duration of the ulceration. The area under the ROC curve was 0.87 (Figure). The total accuracy was 85%, sensitivity 94.6%, specificity 66% and F1 score 89%. Conclusions: ANN can be used in the prediction of the DFS course. The algorithm, which is the source of a digital application, is particularly useful in identifying individuals with diabetic foot ulcerations who fail to be healed in three months. Disclosure A.A. Poradzka: Other Relationship; Self; Boehringer Ingelheim (Canada) Ltd., Urgo Medical. L. Czupryniak: None.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".