The Yield of Computed Tomography of the Head Among Patients Presenting With Syncope: A Systematic Review
Bibliographic record
Abstract
Abstract Background Overuse of head computed tomography (CT) for syncope has been reported. However, there is no literature synthesis on this overuse. We undertook a systematic review to determine the use and yield of head CT and risk factors for serious intracranial conditions among syncope patients. Methods We searched Embase, Medline, and Cochrane databases from inception until June 2017. Studies including adult syncope patients with part or all of patients undergoing CT head were included. We excluded case reports, reviews, letters, and pediatric studies. Two independent reviewers screened the articles and collected data on CT head use, diagnostic yield (proportion with acute hemorrhage, tumors or infarct), and risk of bias. We report pooled percentages, I2, and Cochran's Q‐test. Results Seventeen articles with 3,361 syncope patients were included. In eight ED studies (n = 1,669), 54.4% (95% confidence interval [CI] = 34.9%–73.2%) received head CT with a 3.8% (95% CI = 2.6%–5.1%) diagnostic yield and considerable heterogeneity. In six in‐hospital studies (n = 1,289), 44.8% (95% CI = 26.4%–64.1%) received head CT with a 1.2% (95% CI = 0.5%–2.2%) yield and no heterogeneity. In two articles, all patients had CT (yield 2.3%) and the third enrolled patients ≥ 65 years old (yield 7.7%). Abnormal neurologic findings, age ≥ 65 years, trauma, warfarin use, and seizure/stroke history were identified as risk factors. The quality of all articles referenced was strong. Conclusion More than half of patients with syncope underwent CT head with a diagnostic yield of 1.1% to 3.8%. A future large prospective study is needed to develop a robust risk tool.
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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".