The ‘FLAIR Motor Sign’: FLAIR Signal Abnormality in Precentral Cortex is Useful to Diagnose Adult Global Hypoxic-Ischemic Brain Injury Following Cardiac Arrest
Bibliographic record
Abstract
PURPOSE: The precentral cortex normally demonstrates lower signal intensity compared to remainder of the neocortex on 2D fluid attenuated inversion recovery (FLAIR) images. Loss of this normal hypointensity bilaterally can be seen in patients with adult hypoxic-ischemic brain injury (HIBI). We have named this the 'FLAIR motor' sign (FMS). The performance of this sign for detection of HIBI is evaluated in this case-control study. METHODS: MRI studies of 74 consecutive patients with clinical evidence of HIBI following cardiac arrest formed the 'case' group. Controls comprised of normal MRI studies of an equal number of age and gender matched patients. Two fellowship-trained neuro-radiologists reviewed the MRI studies in a blinded randomized fashion and recorded the presence or absence of 'FLAIR motor' sign. RESULTS: Average time from cardiac arrest to MRI was 7.12 days (range: 1-25 days). The average sensitivity and specificity of 'FLAIR motor' sign for HIBI was 86.49% and 100% respectively. The sign demonstrated excellent inter-reader agreement (kappa >0.8). CONCLUSION: The loss of the normal hypointensity in bilateral pre-central cortex on 2D-FLAIR images is a specific and reliable MRI sign of HIBI in the subacute phase following cardiac arrest in adults.
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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 | 0.004 |
| 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 | 0.001 |
| 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".