Electrodiagnostic subtyping in <scp>Guillain–Barré</scp> syndrome patients in the International <scp>Guillain–Barré</scp> Outcome Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Various electrodiagnostic criteria have been developed in Guillain-Barré syndrome (GBS). Their performance in a broad representation of GBS patients has not been evaluated. Motor conduction data from the International GBS Outcome Study (IGOS) cohort were used to compare two widely used criterion sets and relate these to diagnostic amyotrophic lateral sclerosis criteria. METHODS: From the first 1500 patients in IGOS, nerve conduction studies from 1137 (75.8%) were available for the current study. These patients were classified according to nerve conduction studies criteria proposed by Hadden and Rajabally. RESULTS: Of the 1137 studies, 68.3% (N = 777) were classified identically according to criteria by Hadden and Rajabally: 111 (9.8%) axonal, 366 (32.2%) demyelinating, 195 (17.2%) equivocal, 35 (3.1%) inexcitable and 70 (6.2%) normal. Thus, 360 studies (31.7%) were classified differently. The areas of differences were as follows: 155 studies (13.6%) classified as demyelinating by Hadden and axonal by Rajabally; 122 studies (10.7%) classified as demyelinating by Hadden and equivocal by Rajabally; and 75 studies (6.6%) classified as equivocal by Hadden and axonal by Rajabally. Due to more strictly defined cutoffs fewer patients fulfilled demyelinating criteria by Rajabally than by Hadden, making more patients eligible for axonal or equivocal classification by Rajabally. In 234 (68.6%) axonal studies by Rajabally the revised El Escorial (amyotrophic lateral sclerosis) criteria were fulfilled; in axonal cases by Hadden this was 1.8%. CONCLUSIONS AND DISCUSSION: This study shows that electrodiagnosis in GBS is dependent on the criterion set utilized, both of which are based on expert opinion. Reappraisal of electrodiagnostic subtyping in GBS is warranted.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".