State of the science in diabetic foot: subjective screening vs. objective diabetic neuropathy examination in primary care
Bibliographic record
Abstract
Among the conditioning factors of Diabetic Foot (DF), neuropathy is considered the main factor, arteriopathy the aggravating factor, and foot deformities the triggering factor. The preventive interventions for DF and its complications are distributed by levels of care. At the higher level, hospital care focuses on reducing DF amputations. At the lower level, Primary Care (PC) and Podiatry, focused on preventing DF. PC is considered the ideal place to identify the conditioning factors of DF. In this area, prevention follows the recommendations of the International Working Group on Diabetic Foot (IWGDF) by screening neuropathy focused on the sensitive or insensitive foot. The American Diabetes Association (ADA) a recommends person-centered assessment of neuropathy by clinical examination of symptoms and signs testing sensory, motor, and autonomic neuropathy. This controversy lead us to investigate which methodology (screening or clinical examination) could be more accurate in identifying the conditioning factors of DF in a group of people recruited in the TERMOPIEDI study. Neuropathy was assessed following the definition of diabetic neuropathy, the Young MJ diagnostic criteria, and the Toronto Council diagnostic category. These results allowed us to know the applicability of this procedure in PC within nursing competencies, detecting a greater number of patients with neuropathy compared to the screening method. People with neuropathy presented higher plantar temperature, concluding that neuropathy interferes with foot thermoregulation.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".