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 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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".