Diagnostic Sensitivity of F-wave Parameters in Unilateral S1 Radiculopathy
Bibliographic record
Abstract
Background: F-wave study, part of electrodiagnostic study, has had a controversial sensitivity in the diagnosis of lumbosacral radiculopathy.Objectives: We aimed to compare F wave parameters obtained from the tibial nerve of both extremities in patients with unilateral S1 radiculopathy. Materials and Methods:The study was done from March to September 2015 in the Electrodiagnostic laboratory of an academic hospital affiliated to Isfahan University of Medical Sciences.19 consecutive patients with clinically and electromyographically approved diagnosis of unilateral S1 radiculopathy entered the study.F-wave parameters (F minimum latency, F maximum latency, F chronodispersion and F persistence) were recorded from tibial nerve of both extremities.Patients with diabetes, bilateral S1 radiculopathy or any other disease known to affect peripheral nerves were excluded from the study.Results: Of nineteen participants, 11 were men.Their mean±SD of age was 46.6±13.7 years.There were no significant differences between mean of F wave parameters recorded from affected and unaffected sides.Also, it was shown that, there was a positive correlation between these parameters in two extremities. Conclusion:The current study compared various F-wave parameters and the results did not support employing F-wave study as a sensitive method for detecting unilateral S1 radiculopathy.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".