Diffusion tensor imaging assessment of microstructural brainstem integrity in Chiari malformation Type I
Bibliographic record
Abstract
OBJECTIVE The diagnosis of Chiari malformation Type I (CM-I) is primarily based on the degree of cerebellar tonsillar herniation even though it does not always correlate with symptoms. Neurological dysfunction in CM-I presumably results from brainstem compression. With the premise that conventional MRI does not reveal brain microstructural changes, this study examined both structural and microstructural neuroimaging metrics to distinguish patients with CM-I from age- and sex-matched healthy control subjects. METHODS Eight patients with CM-I and 16 controls were analyzed. Image postprocessing involved coregistration of anatomical T1-weighted with diffusion tensor images using 3D Slicer software. The structural parameters included volumes of the posterior fossa, fourth ventricle, and tentorial angle. Fractional anisotropy (FA) was calculated separately in the anterior and posterior compartments of the lower brainstem. RESULTS The mean age of patients in the CM-I cohort was 42.6 ± 10.4 years with mean tonsillar herniation of 12 mm (SD 0.7 mm). There were no significant differences in the posterior fossa volume (p = 0.06) or fourth ventricular volume between the 2 groups (p = 0.11). However, the FA in the anterior brainstem compartment was significantly higher in patients with CM-I preoperatively (p = 0.001). The FA values normalized after Chiari decompression except for persistently elevated FA in the posterior brainstem compartment in patients with CM-I and syrinx. CONCLUSIONS In this case-control study, microstructural alterations appear to be reliably associated with the diagnosis of CM-I, with a significantly elevated FA in the lower brainstem in patients with CM-I compared with controls. More importantly, the FA values normalized after decompressive surgery. These findings should be validated in future studies to determine the significance of diffusion tensor imaging-based assessment of brainstem microstructural integrity as an adjunct to the clinical assessment in patients with CM-I.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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 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".