Treatment-related fluctuations in Guillain-Barré syndrome: clinical features and predictors of recurrence
Bibliographic record
Abstract
BACKGROUND: A treatment-related fluctuation (TRF) in a patient with Guillain-Barré syndrome (GBS) is defined as clinical deterioration within two months of symptom onset following previous stabilization or improvements with treatment. OBJECTIVE: To investigate the clinical characteristics and factors that could increase the risk of relapse of GBS in patients with and without TRFs. METHODS: Retrospective review of medical records of patients (>18 years) with GBS evaluated between January/2006 and July/2019. Demographic and clinical characteristics, ancillary studies, treatment received, and the clinical course of patients with and without TRFs were analyzed. RESULTS: Overall, 124 cases of GBS were included; seven (5.6%) presented TRFs. GBS-TRF cases were triggered more frequently by infectious mononucleosis (28.57 vs. 8.55%; p=0.01). GBS-TRF were initially treated with plasmapheresis more frequently than those without TRF (14.29 vs. 1.70%; p=0.0349). Combined treatment (71.43 vs. 4.27%; p<0.001) and corticosteroids (42.86 vs. 1.71%; p<0.001) were more commonly used in the GBS-TRF group. GBS-TRF patients presented a higher median initial disability score (4 vs. 2; p=0.01). CONCLUSIONS: Patients with GBS triggered by infectious mononucleosis and a high degree of initial disability have higher chances of developing TRFs. Although patients with TRF were treated with plasmapheresis more often, the total number was too low to suggest a link between plasma exchange and TRF.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".