Predicting Outcome in Guillain-Barré Syndrome
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The clinical course and outcome of the Guillain-Barré syndrome (GBS) are diverse and vary among regions. The modified Erasmus GBS Outcome Score (mEGOS), developed with data from Dutch patients, is a clinical model that predicts the risk of walking inability in patients with GBS. The study objective was to validate the mEGOS in the International GBS Outcome Study (IGOS) cohort and to improve its performance and region specificity. METHODS: We used prospective data from the first 1,500 patients included in IGOS, aged ≥6 years and unable to walk independently. We evaluated whether the mEGOS at entry and week 1 could predict the inability to walk unaided at 4 and 26 weeks in the full cohort and in regional subgroups, using 2 measures for model performance: (1) discrimination: area under the receiver operating characteristic curve (AUC) and (2) calibration: observed vs predicted probability of being unable to walk independently. To improve the model predictions, we recalibrated the model containing the overall mEGOS score, without changing the individual predictive factors. Finally, we assessed the predictive ability of the individual factors. RESULTS: For validation of mEGOS at entry, 809 patients were eligible (Europe/North America [n = 677], Asia [n = 76], other [n = 56]), and 671 for validation of mEGOS at week 1 (Europe/North America [n = 563], Asia [n = 65], other [n = 43]). AUC values were >0.7 in all regional subgroups. In the Europe/North America subgroup, observed outcomes were worse than predicted; in Asia, observed outcomes were better than predicted. Recalibration improved model accuracy and enabled the development of a region-specific version for Europe/North America (mEGOS-Eu/NA). Similar to the original mEGOS, severe limb weakness and higher age were the predominant predictors of poor outcome in the IGOS cohort. DISCUSSION: mEGOS is a validated tool to predict the inability to walk unaided at 4 and 26 weeks in patients with GBS, also in countries outside the Netherlands. We developed a region-specific version of mEGOS for patients from Europe/North America. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that the mEGOS accurately predicts the inability to walk unaided at 4 and 26 weeks in patients with GBS. TRIAL REGISTRATION INFORMATION: NCT01582763.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it