Diagnostic Tools for Diabetic Sensorimotor Polyneuropathy
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
Diabetes and its complications are major causes of mortality in the United States, with increasing rates of morbidity and increasing health care costs. Patients diagnosed with diabetes attempt to control cholesterol levels, blood pressure, and blood glucose levels to decrease the risk of diabetic microvascular complications (DMC), such as diabetic sensorimotor polyneuropathy (DSP) [also known as diabetic peripheral neuropathy (DPN)]. Despite control of these risk factors for vascular disease, many patients still develop DSP. Research investigating diabetic neuropathy holds promise for specific treatment of diabetic complications. Intrinsic to the success of new therapies is the accurate diagnosis and evaluation of DSP. Symptom scores, quantitative sensory testing and electrophysiology are some of the diagnostic tools to identify the signs and symptoms of DSP. Early detection of neuropathy enables clinicians to prevent long-term complications like ulcers and amputations in patients with diabetes. The focus of this review is to describe the composite of tools necessary for diagnosis of DSP.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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