Screening for the High-Risk Diabetic Foot
Classification
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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In Brief PURPOSE: To enhance the learner’s competence with knowledge of screening for the high-risk diabetic foot. TARGET AUDIENCE: This continuing education activity is intended for physicians and nurses with an interest in skin and wound care. OBJECTIVES: After participating in this educational activity, the participant should be better able to: 1. Demonstrate use of the 60-second tool and other foot assessment strategies to identify risk in the diabetic foot. 2. Apply the 60-second tool positive screen recommendations and accepted evidence-based treatment guidelines in patient care situations. People with diabetes mellitus will develop lower-limb complications, such as neuropathy, peripheral vascular disease, foot ulcers, and lower-leg amputations. Resources to control elevated hemoglobin A1c and blood pressure, along with the standardized approach using the 60-second tool (2012)©, can detect the high-risk diabetic foot and help prevent complications. This continuing education activity discusses the 60-second tool (2012)© that the authors have developed to identify the high-risk patients with diabetes and provide guidance for appropriate interprofessional care.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it