Acute ischaemic stroke or transient ischaemic attack and the need for inpatient echocardiography
Bibliographic record
Abstract
OBJECTIVES: To determine the diagnostic yield of echocardiography and its utility in changing medical management; and to derive a risk score to guide its use in patients with in-hospital stroke or transient ischaemic attack (TIA). METHODS: We carried out a retrospective chart review from January 2009 to June 2010 of patients with acute ischaemic stroke or TIA who had undergone transthoracic echocardiography (TTE) or transoesophageal echocardiography (TOE). Clinical and imaging findings at baseline were noted and 'potential clinically relevant findings' identified on TTE and TOE. A multivariable logistic regression was used to identify predictors of potential clinically relevant findings on TTE or TOE and derive a risk score. RESULTS: Of 370 patients, 307 (83.0%) had TTE and 63 (17.0%) had additional TOE. Potential clinically relevant findings on echocardiography were noted in 28 (7.6%) patients. Change in medical management was noted in 19/307 (6.2%) patients on TTE and in 7/63 (11.1%) patients on TOE. Male sex (OR 3.05, 95% CI 1.19 to 7.84; p=0.021), abnormal admission ECG (OR 4.39, 95% CI 1.79 to 10.79; p=0.001), and embolic pattern imaging at baseline (OR 2.38, 95% CI 1.05 to 5.40; p=0.038) were independent predictors of findings on TTE or TOE. A risk score including these three variables had modest discrimination (c-statistic 0.69, 95% CI 0.59 to 0.80). CONCLUSIONS: Echocardiography detected potential clinically relevant findings in a minority of patients (7.6%), but these findings changed medical management 90.5% of the time. A risk score using sex, ECG abnormality, and embolic pattern imaging at baseline could help predict which patients are more likely to have these echo findings.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".