Evaluating yield and utilization of ganglioside antibody testing in clinical practice
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Ganglioside antibodies can help diagnose distinct acute and chronic inflammatory neuropathies including axonal variants of Guillain-Barre syndrome, Miller-Fisher syndrome (MFS), multifocal motor neuropathy, and chronic sensory ataxic neuropathies. Because ganglioside antibody testing may be routinely ordered in patients with suspected inflammatory neuropathy, we sought to evaluate its yield and utilization in clinical practice. METHODS: We performed a retrospective chart review of all patients at London Health Sciences Centre who underwent ganglioside antibody testing between April 2019 and August 2023. The disease phenotype was determined for each patient, and the proportion of all tests that yielded a true-positive result was calculated. Ganglioside antibody positivity was classified as a true-positive result if the disease phenotype was robustly associated with the detected ganglioside antibody and there was no other more likely diagnosis. RESULTS: We identified 92 patients who underwent ganglioside antibody testing. One patient (1%) was classified as having a true-positive result; this patient had GQ1b-IgG positivity with MFS. Among 92 patients tested, 20 patients (22%) had a disease phenotype that was considered to be robustly associated with ganglioside antibody positivity. CONCLUSIONS: The yield of ganglioside antibody testing in clinical practice is low. We found that this testing is frequently ordered in patients with disease phenotypes that are not robustly associated with ganglioside antibody positivity, indicating that suboptimal test utilization is a primary contributor to its low yield. Restricting ganglioside antibody testing to patients with characteristic disease phenotypes would be valuable to improving yield and utilization of this testing.
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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 | 0.009 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".