Bibliographic record
Abstract
Diabetic distal symmetric polyneuropathy (diabetic DSP) has variable clinical presentation that can complicate the diagnostic process. It is primarily identified by asymptomatic annual screening or from neuropathic symptoms. In this chapter, we present key considerations for findings on screening or clinical evaluation. First, identification of risk factors for diabetic DSP establishes a general pre-assessment probability. Second, identification of the other component causes (foot deformity, vascular impairment) of foot complications along with identifying the impaired protective sensation that is part of diabetic DSP is essential for preventing foot outcomes. Third, the clinician must recognize that there is heterogeneity in manifestations, involving small and large nerve fiber types. As in any process of diagnosis, a clinical evaluation considers each symptom or sign’s contribution to incrementally revising the clinician’s estimates of disease probability and it reduces clinical uncertainty. Simple screening methods are valid, as are clinical scales, adopted into research cohorts and trials, that can be implemented into practice. Once a chronically-progressive distal symmetric pattern of polyneuropathy is confidently identified, alternate causes can generally be accomplished by simple clinical considerations and simple laboratory testing. While uncommon, a typical features such as asymmetry, nonlength dependence, acute or subacute rather than chronic onset and progression, and motor predominance call for specialized testing and clinical expertise from a neurologist. Depending on the number and severity of deformity, vascular insufficiency, and diabetic DSP’s impairment in protective sensation, interventions are initiated including self-foot care education and professionally-fitted therapeutic footwear to referral for wound management and surgical consultation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".