Added Value of MRI over CT of the Brain in Intensive Care Unit Patients
Bibliographic record
Abstract
BACKGROUND: Intensive care unit (ICU) patients with neurological impairments often require neuroimaging. However, the relative sensitivity of various imaging modalities of the brain has not yet been explored in this population. METHODS: In this study, we compare the findings of CT and MRI scans in ICU patients to (1) identify the number and rate of clinically relevant lesion detected by MRI while missed by CT and vice versa and (2) determine specific lesion types for which CT versus MRI discrepancies exist. A review of medical records included CT and MRI reports of patients who underwent these procedures while they were patients in our ICUs between July 2004 and July 2009. MRI and CT were compared regarding their ability to detect clinically relevant abnormalities. Odds ratios with 95% confidence limits were calculated to compare diagnostic categories regarding the rate of discrepant MRI versus CT findings, followed by power analyses to estimate sample sizes necessary to allow for further testing in a larger trial. RESULTS: MRI revealed clinically relevant additional abnormalities over CT in 129 of 136 patients (95%) that included the detection of additional finding for 15/27 hemorrhagic lesions (55.6%), 33/36 (92%) ischemic strokes, 19/27 (70%) traumatic lesions, 8/14 (57%) infections, 15/24 (62.5%) metabolic abnormalities, and all seven neoplasms. Odds ratio analysis revealed the added sensitivity of MRI to be greater for ischemic and neoplastic lesions than for trauma, metabolic-related abnormalities, infection, or hemorrhage. CONCLUSIONS: MRI is more sensitive than CT in identifying clinically meaningful lesions in at least a subset of ICU patients, regardless of pathology.
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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 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".