Minimum detectable difference of MR diffusion maps in acute ischemic stroke
Bibliographic record
Abstract
PURPOSE: To determine the minimum detectable difference (MDD) and investigate variability of region-of-interest (ROI) analysis of apparent diffusion coefficient (ADC) and fractional anisotropy (FA) in acute ischemic stroke. MATERIALS AND METHODS: Ten patients with acute stroke (<24 hours) and moderate-to-large infarcts were imaged using a fast diffusion tensor technique. Four observers repeated three trials, during which each of two ROI types (free-hand polygon and ellipse) were drawn in white and gray matter (WM and GM) on FA and ADC maps. Analysis-of-variance techniques examined tissue and ROI type effects as well as inter- and intraobserver variability. F-tests examined the variability differences between ROI types. RESULTS: The MDD for ADC was 0.160 x 10(-3) mm(2) s(-1) in WM and 0.212 x 10(-3) mm(2) s(-1) in GM. The FA MDD was 0.19 in WM and 0.10 in GM. Tissue but not ROI type affected the mean values for both ADC and FA maps. Intraobserver reliability was substantial, while interobserver reliability was poor-to-moderate. No variability differences were found by ROI types. CONCLUSION: The MDD for WM and GM in normal and ischemic tissue were calculated. Inter- and intraobserver variability and tissue type affect ROI analysis of ADC and FA maps.
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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 | 0.003 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".