Diabetic polyneuropathies: update on research definition, diagnostic criteria and estimation of severity
Bibliographic record
Abstract
Prior to a joint meeting of the Neurodiab Association and International Symposium on Diabetic Neuropathy held in Toronto, Ontario, Canada, 13-18 October 2009, Solomon Tesfaye, Sheffield, UK, convened a panel of neuromuscular experts to provide an update on polyneuropathies associated with diabetes (Toronto Consensus Panels on DPNs, 2009). Herein, we provide definitions of typical and atypical diabetic polyneuropathies (DPNs), diagnostic criteria, and approaches to diagnose sensorimotor polyneuropathy as well as to estimate severity. Diabetic sensorimotor polyneuropathy (DSPN), or typical DPN, usually develops on long-standing hyperglycaemia, consequent metabolic derangements and microvessel alterations. It is frequently associated with microvessel retinal and kidney disease-but other causes must be excluded. By contrast, atypical DPNs are intercurrent painful and autonomic small-fibre polyneuropathies. Recognizing that there is a need to detect and estimate severity of DSPN validly and reproducibly, we define subclinical DSPN using nerve conduction criteria and define possible, probable, and confirmed clinical levels of DSPN. For conduct of epidemiologic surveys and randomized controlled trials, it is necessary to pre-specify which attributes of nerve conduction are to be used, the criterion for diagnosis, reference values, correction for applicable variables, and the specific criterion for DSPN. Herein, we provide the performance characteristics of several criteria for the diagnosis of sensorimotor polyneuropathy in healthy subject- and diabetic subject cohorts. Also outlined here are staged and continuous approaches to estimate severity of DSPN.
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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 | 0.026 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".