Ulnar F-wave Study in the Detection of Subclinical Diabetic Neuropathy
Bibliographic record
Abstract
Background: Diabetic peripheral neuropathy (DPN) brought on by damage to the peripheral sensory and motor nerves. Since the F-wave passes through both the afferent and efferent pathways in the motor nerve, alterations in the F response parameters may indicate injury to either of these pathways.Objective: To explore the effectiveness of ulnar F-wave parameters in diagnosing subclinical neuropathy in patients with type 2 diabetes mellitus (T2DM).Methods: The study examined F-wave, glycated hemoglobin (HbA1c), and modified Toronto clinical neuropathy score (mTCNS), in addition to sural to radial amplitude ratio, sensory and motor conduction of upper and lower limb nerves, in 116 T2DM patients and 121 control participants.Results: In comparison to controls, DPN patients exhibit longer F-minimum (Fmin), F-maximum (Fmax), F-ratio (Fr), and modified F-ratio (mFr). Along with higher F-chronodispersion (Fc) and F-estimate (Fe), they also show lower F-persistence (Fp), F-wave conduction velocity (FWCV), and F-index (Fi). Patients with DPN have higher F-estimate (Fe) values than the controls, whereas patients without DPN have lower Fi. There was a strong correlation observed between several F-wave parameters and the mTCNS, disease duration, and HbA1c.Conclusion: Increased mFr value, longer Fm latency, and higher Fi value were useful in the early identification of subclinical DPN, while higher Fi value, FWCV slowing, and prolonged Fmin and Fmax latencies helped identify patients with clinical DPN. When separating diabetics with T2DM from those without DPN, the Fi value has the highest sensitivity and specificity.Keywords: DM, subclinical neuropathy, ulnar nerve, F-wave.Citation: Abdul Qader AA, Hamdan FB, Khudhair MS. Ulnar F-wave study in the detection of subclinical diabetic neuropathy. Iraqi JMS. 2025; 23(2): 282-296. doi: 10.22578/IJMS.23.2.11
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 | 0.023 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".