Diagnostic yield of <scp>CT</scp> head in delirium and altered mental status—A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background CT head is commonly performed in the setting of delirium and altered mental status (AMS), with variable yield. We aimed to evaluate the yield of CT head in hospitalized patients with delirium and/or AMS across a variety of clinical settings and identify factors associated with abnormal imaging. Methods We included studies in adult hospitalized patients, admitted to the emergency department (ED) and inpatient medical unit (grouped together) or the intensive care unit (ICU). Patients had a diagnosis of delirium/AMS and underwent a CT head that was classified as abnormal or not. We searched Medline, Embase and other databases (informed by PRISMA guidelines) from inception until November 11, 2021. Studies that were exclusively performed in patients with trauma or a fall were excluded. A meta‐analysis of proportions was performed; the pooled proportion of abnormal CTs was estimated using a random effects model. Heterogeneity was determined via the I 2 statistic. Factors associated with an abnormal CT head were summarized qualitatively. Results Forty‐six studies were included for analysis. The overall yield of CT head in the inpatient/ED was 13% (95% CI: 10.2%–15.9%) and in ICU was 17.4% (95% CI: 10%–26.3%), with considerable heterogeneity (I 2 96% and 98% respectively). Heterogeneity was partly explained after accounting for study region, publication year, and representativeness of the target population. Yield of CT head diminished after year 2000 (19.8% vs. 11.1%) and varied widely depending on geographical region (8.4%–25.9%). The presence of focal neurological deficits was a consistent factor that increased yield. Conclusion Use of CT head to diagnose the etiology of delirium and AMS varied widely and yield has declined. Guidelines and clinical decision support tools could increase the appropriate use of CT head in the diagnostic etiology of delirium/AMS.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".