Diagnostic value of clinical deep tendon reflexes in diabetic peripheral neuropathy
Bibliographic record
Abstract
Introduction: The aim of the present study was to evaluate the diagnostic efficacy of different tendon reflexes in detecting diabetic peripheral neuropathy (DPN). Material and methods: According to the changes in tendon reflexes, all patients with diabetes were divided into three strata: impaired Achilles reflex only, impaired lower extremity reflexes, and impaired lower and upper extremity reflexes. Taking nerve conduction studies (NCS) as the gold standard, the sensitivity, specificity, and predictive ability of the tendon reflexes of these three strata, as well as the Toronto clinical scoring system (TCSS) and Michigan Neuropathy Screening Instrument (MNSI), were calculated. Then, the electrophysiological characteristics of diabetic patients with different tendon reflexes were analysed. Results: Among the 240 patients studied, 92 (38.3%) presented evidence of neuropathy, which was confirmed by abnormal NCS, while 148 (61.7%) had normal NCS results. Taking NCS as the gold standard, stratum 1 yielded a sensitivity and specificity of 93.5% and 54.7%, respectively, while stratum 3 had higher specificity (96.6%) and lower sensitivity (34.8%) when compared to stratum 1. However, stratum 2 had the highest specificity (75.7%). Conclusions: The assessment of tendon reflexes can be proposed as a test for screening diabetic polyneuropathy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".