“Unequivocally Abnormal” vs “Usual” Signs and Symptoms for Proficient Diagnosis of Diabetic Polyneuropathy
Bibliographic record
Abstract
OBJECTIVE To repeat the Clinical vs Neurophysiology (Cl vs N Phys) trial using "unequivocally abnormal" signs and symptoms (Trial 2) compared with the earlier trial (Trial 1), which used "usual" signs and symptoms. DESIGN Standard and referenced nerve conduction abnormalities were used in both Trials 1 and 2 as the standard criterion indicative of diabetic sensorimotor polyneuropathy. Physician proficiency (accuracy among evaluators) was compared between Trials 1 and 2. SETTING Academic medical centers in Canada, Denmark, England, and the United States. PARTICIPANTS Thirteen expert neuromuscular physicians. One expert was replaced in Trial 2. RESULTS The marked overreporting, especially of signs, in Trial 1 was avoided in Trial 2. Reproducibility of diagnosis between days 1 and 2 was significantly (P = .005) better in Trial 2. The correlation of the following clinical scores with composite nerve conduction measures spanning the range of normality and abnormality was improved in Trial 2: pinprick sensation (P = .03), decreased reflexes (P = .06), touch-pressure sensation (P = .06), and the sum of symptoms (P = .06). CONCLUSIONS The simple pretrial decision to use unequivocally abnormal signs and symptoms-taking age, sex, and physical variables into account-in making clinical judgments for the diagnosis of diabetic sensorimotor polyneuropathy (Trial 2) improves physician proficiency compared with use of usual elicitation of signs and symptoms (Trial 1); both compare to confirmed nerve conduction abnormality.
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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 | 0.023 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| 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".