Laboratory Performance on Reporting Monoclonal Gammopathy During Cerebrospinal Fluid Oligoclonal Banding Analysis from External Quality Assessment Surveys
Bibliographic record
Abstract
OBJECTIVE: Cerebrospinal fluid (CSF) oligoclonal banding (OCB) analysis is a sensitive test used to mainly aid multiple sclerosis (MS) diagnosis. Monoclonal gammopathy is usually an incidental finding during CSF OCB analysis. The aim of this study was to assess laboratory performance on reporting monoclonal gammopathy pattern during CSF OCB analysis based on external quality assessment surveys. METHODS: The CSF OCB surveys from the College of American Pathologists (CAP) from 2010 to 2015 were reviewed. The UK National External Quality Assessment Service (NEQAS) CSF OCB surveys from 2014 to 2017 were also reviewed. All monoclonal gammopathy patterns were confirmed by serum protein electrophoresis followed by immunofixation on a Sebia Hydrasys analyzer. RESULTS: There were 11 monoclonal gammopathy cases identified in the CAP OCB survey from 2010 to 2015. The average rate of CAP participants that correctly reported the pattern was 25.1% (range, 2.4%-66.7%). The most common pattern incorrectly reported was the systemic inflammation pattern, followed by the oligoclonal bands present/positive pattern. The NEQAS OCB survey from 2014 to 2017 had 4 monoclonal gammopathy cases and indicated a much higher number (average, 88.5%; range, 84.1%-90.8%) of participating laboratories to successfully detect monoclonal gammopathy. CONCLUSION: Monoclonal gammopathy is still an underrecognized pattern in the CSF OCB analysis by the CAP participating laboratories and warrants further education.
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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 | 0.023 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".