Use of Brain Imaging (Computed Tomography and Magnetic Resonance Imaging) in First-Episode Psychosis: Review and Retrospective Study
Bibliographic record
Abstract
OBJECTIVE: To identify and review available evidence on the diagnostic yield of brain computed tomographies (CTs) and magnetic resonance images (MRIs) in first-episode psychosis, and examine yield in our own institution (Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, Quebec). METHOD: Using MEDLINE (1966 to October 2007) and EMBASE (1980 to October 2007), we identified and analyzed studies that examined imaging yields in first-episode psychosis; yield being defined as the percentage of scans showing abnormalities that may result in psychosis. We also retrospectively analyzed diagnostic yields in 46 patients hospitalized in our institution between 2001 and 2006 for first-episode psychosis. RESULTS: Five studies were deemed relevant. Including our own series, the sample comprised 384 CT and 184 MRI scans. Point estimate for diagnostic yield was 1.3% for CT and 1.1% for MRI scans. These yields likely overestimate clinical usefulness of findings. MRI scans also resulted in a sizeable number of fortuitous, clinically irrelevant findings. CONCLUSIONS: In first-episode psychosis, routine CT or MRI scans are of little benefit and should be reserved for situations where history or examination suggests neurological causation, or possibly for people aged 50 years and older.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".