Electrodiagnostic subtyping in <scp>Guillain‐Barré</scp> syndrome: Use of criteria in practice based on a survey study in <scp>IGOS</scp>
Bibliographic record
Abstract
Electrodiagnostic (EDx) studies are helpful in diagnosing and subtyping of Guillain-Barré syndrome (GBS). Published criteria for differentiation into GBS subtypes focus on cutoff values, but other items receive less attention, although they may influence EDx subtyping: (a) extensiveness of EDx testing, (b) nerve-specific considerations, (c) distal compound muscle action potential (CMAP)-amplitude requirements, (d) criteria for conduction block and temporal dispersion. The aims of this study were to investigate how these aspects were approached by neuromuscular EDx experts in practice and how this was done in previously published EDx criteria for GBS. A completed questionnaire was returned by 24 (of 49) members of the electrophysiology expertise group from the International GBS Outcome Study. Six published EDx criteria for GBS subtyping were compared regarding these aspects. The indicated minimal number of motor nerves to study varied among respondents and tended to be more extensive in equivocal than normal studies. Respondents varied considerably regarding usage of compression sites for subtyping (median/wrist, ulnar/elbow, peroneal/fibular head): 29% used all variables from all sites, 13% excluded all sites, and 58% used only some sites and/or variables. Thirty-eight percent of respondents required a minimal distal CMAP amplitude to classify distal motor latency as demyelinating, and 58% did for motor conduction velocity. For proximal/distal CMAP-amplitude ratio and F-wave latency, a requisite minimal CMAP amplitude was more often required (79%). Also, the various published criteria sets showed differences on all items. Practical use of EDx criteria for subtyping GBS vary extensively across respondents, potentially lowering the reproducibility of GBS subtyping.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".