P.062 MR Venography predicts increased intracranial hypertension in children with hydrocephalus
Bibliographic record
Abstract
Background: We investigated whether the presence of dural sinus narrowing is a more reliable marker of intracranial hypertension / shunt failure in children than the imaging finding of hydrocephalus. -Methods: Cranial MRIs of n=12 children were included when being well and when there was definitive intracranial hypertension as per follow-up and intraoperative results (gold standard). Images werde assessed for hydrocephalus on T2w images and narrowing of dural sinuses on MR vengraphy (diameter of <50%). Results: Dural sinuses narrowing was detected with a sensitivity of 0.67, a specificity of 1.0, PPV of 1.0 and NPV of 0.75 (Table 1). Hydrocephalus was detected with a sensitivity of 0.5, a specificity of 0.83, PPV of 0.75 and NPV of 0.63. Results differed between the test methods (p = 0.01, Cochrane Q test). Conclusions: Dural sinus narrowing more reliably predicted intracranial hypertension, a sign which might significantly improve care in critically ill children. Age at MRI Shuntfailure as per clinical follow-up (Goldstandard) Hydro cephalus Dural Sinus Narrowing Patient # Years 1 = yes 2 = no 1 = yes 2 = no 1 = yes 2 = no 1 1 1 1 0 4 0 0 0 2 6 1 1 1 6 0 0 0 3 12 1 0 1 12 0 0 0 4 18 1 0 1 19 0 0 0 5 0 1 1 0 1 0 1 0 6 0 0 1 0 0 1 1 0 7 17 1 0 1 17 0 0 0 8 10 1 1 1 10 0 0 0 9 0 1 1 1 1 0 0 0 10 8 1 0 1 8 0 0 0 11 14 1 0 1 14 0 0 0 12 18
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".