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.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".