The clinical utility of structural neuroimaging in first-episode psychosis: A systematic review
Bibliographic record
Abstract
BACKGROUND: Australian and US guidelines recommend routine brain imaging, either computed tomography or magnetic resonance imaging, to exclude structural lesions in presentations for first-episode psychosis. The aim of this review was to examine the evidence for the appropriateness and clinical utility of this recommendation by assessing the frequency of abnormal radiological findings in computed tomography and magnetic resonance imaging scans among patients with first-episode psychosis. METHODS: PubMed and Embase database were searched from inception to April 2018 using appropriate MeSH or Emtree terms. Studies were included in the review if they reported data on computed tomography or magnetic resonance imaging scan findings of individuals with first-episode psychosis. No restriction on the geographical location of the study or the age of participants was applied. We calculated the percentage of abnormal radiological findings in each study, separately by the two diagnostic methods. RESULTS: There were 16 suitable studies published between 1988 and 2017, reporting data on an overall 2312 patients with first-episode psychosis. Most were observational studies with a retrospective design and the majority examined patients with computed tomography. While structural abnormalities were a relatively common finding, these rarely required clinical intervention (range across studies: 0-60.7%; median: 3.5%) and were very rarely the cause of the psychotic symptoms (range: 0-3.3%; median: 0%). Only 2 of the 16 studies concluded that brain imaging should be routinely ordered in first-episode psychosis. CONCLUSION: There is insufficient evidence to suggest that brain imaging should be routinely ordered for patients presenting with first-episode psychosis without associated neurological or cognitive impairment. The appropriate screening procedure for structural brain lesions is conventional history-taking, mental status and neurological examination. If intracranial pathology is suspected clinically, a magnetic resonance imaging or computed tomography scan should be performed depending on the clinical signs, the acuity and the suspected pathology. National guidelines should reflect evidence-based data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".