Does This Patient With Diabetes Have Large-Fiber Peripheral Neuropathy?
Bibliographic record
Abstract
CONTEXT: Diabetic peripheral neuropathy predisposes patients to foot ulceration that heals poorly and too often leads to amputation. Large-fiber peripheral neuropathy (LFPN), one common form of diabetic neuropathy, when detected early prompts aggressive measures to prevent progression to foot ulceration and its associated morbidity and mortality. OBJECTIVE: To systematically review the literature to determine the clinical examination findings predictive of asymptomatic LFPN before foot ulceration develops. DATA SOURCES, STUDY SELECTION, AND DATA EXTRACTION: MEDLINE (January 1966-November 2009) and EMBASE (1980-2009 [week 50]) databases were searched for articles on bedside diagnosis of diabetic peripheral neuropathy. Included studies compared elements of history or physical examination with nerve conduction testing as the reference standard. DATA SYNTHESIS: Of 1388 articles, 9 on diagnostic accuracy and 3 on precision met inclusion criteria. The prevalence of diabetic LFPN ranged from 23% to 79%. A score greater than 4 on a symptom questionnaire developed by the Italian Society of Diabetology increases the likelihood of LFPN (likelihood ratio [LR], 4.0; 95% confidence interval [CI], 2.9-5.6; negative LR, 0.19; 95% CI, 0.10-0.38). The most useful examination findings were vibration perception with a 128-Hz tuning fork (LR range, 16-35) and pressure sensation with a 5.07 Semmes-Weinstein monofilament (LR range, 11-16). Normal results on vibration testing (LR range, 0.33-0.51) or monofilament (LR range, 0.09-0.54) make LFPN less likely. Combinations of signs did not perform better than these 2 individual findings. CONCLUSIONS: Physical examination is most useful in evaluating for LFPN in patients with diabetes. Abnormal results on monofilament testing and vibratory perception (alone or in combination with the appearance of the feet, ulceration, and ankle reflexes) are the most helpful signs.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".