The authors respond to criticisms of their model parameters
Bibliographic record
Abstract
Objective To evaluate the prevalence of prior inflammatory events in patients consulting for a first inflammatory neurological event and improve early diagnosis of multiple sclerosis. Methods During the initial visit, the neurologist gave patients a self-administered questionnaire containing 72 questions regarding previous symptoms lasting >24 h. During the follow-up visit, the neurologist validated the symptoms and collected information about the current attack. Results The cohort included 178 patients (74% women, mean age (SD) 33.7 (10.1) years). The main reason for the initial visit was visual disturbance and sensory troubles in limbs. Mean (SD) global Expanded Disability Status Scale score was 1.4 (1.1), 46% of brains MRIs were positive according to Barkhof–Tintoré criteria, 41% had abnormal white blood cell count in cerebrospinal fluid and 71% had immunoglobin G oligoclonal bands. Prior symptoms suggestive of demyelination were reported by 79 patients (44%), validated by the neurologist for 70% (55 patients) and identified only by the neurologist in four patients. Sequelae were observed in 14 patients with validated prior symptoms (26%). The self-administered questionnaire showed an overall sensitivity of 93% and specificity of 80% for identifying patients with prior symptoms suggestive of demyelination. Conclusion A patient-administered questionnaire subsequently validated by the neurologist demonstrated that 33% of patients consulting for a first demyelinating event had prior symptoms suggestive of central nervous system demyelination that had gone unnoticed, and almost 70% had either sequelae of prior demyelination or McDonald criteria for dissemination in space. Such a questionnaire could be a useful tool for earlier diagnosis of multiple sclerosis.
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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 | 0.016 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.039 | 0.019 |
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".