How can MRI descriptors be optimally combined to predict idiopathic intracranial hypertension?
Bibliographic record
Abstract
OBJECTIVES: To establish how MRI descriptors on standard MRI sequences can be optimally combined to predict idiopathic intracranial hypertension (IIH). METHODS: A retrospective single-institution cross-sectional study evaluated consecutive IIH patients undergoing MRI between 2002 and 2015 and a control group. Six established and 8 exploratory MRI descriptors were evaluated. Two observers independently analysed MRI descriptors on T1w sagittal and T2w axial sequences while blinded to clinical data with consensus obtained. Inter-rater reliability was calculated, and the presence of MRI descriptors was compared between IIH patients and controls (Bonferroni correction, P < 0.004). Forward stepwise logistic regression determined which combination of MRI descriptors best predicted IIH. RESULTS: Fifty-four IIH patients (mean age 31.2, standard deviation 10.2, 3 men) and 54 control subjects (mean age 31.7, standard deviation 7.1, 3 men) were evaluated. There was excellent inter-rater reliability for 13/14 MRI descriptors. There were 4/6 established and 6/8 exploratory MRI descriptors associated with IIH (P < 0.004). The optimal combination of descriptors to predict IIH was vertical tortuosity of the optic nerve, enlarged optic nerve sheath, globe flattening score ≥ 2, Yuh score ≥ 3, cervical skin folding, and cervical fat thickness ≥ 10.5 mm. The model correctly classified 93.5% of cases (sensitivity 94.4%, specificity 92.6%, area under the receiver operating characteristic curve [AUC] 0.965). CONCLUSIONS: Evaluating a combination of vertical tortuosity of the optic nerve, enlarged optic nerve sheath, globe flattening, Yuh score, cervical skin folding, and cervical fat thickness optimally predicts IIH. ADVANCES IN KNOWLEDGE: New MRI features are validated for the diagnosis of IIH and the optimal combination for diagnosis is established. REGISTRATION NUMBER: N006 (local institutional review). The full study protocol can be requested from the corresponding author.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".