Impact of rubidium imaging availability on management of patients with acute chest pain
Bibliographic record
Abstract
OBJECTIVE: Evaluate the impact of 82-Rubidium positron emission tomography (PET) myocardial perfusion imaging (MPI) availability on patient management presenting at the emergency department (ED) with chest pain (CP). METHODS: This is a single-center retrospective study of clinical databases. Patients presenting with CP with a non-definitive suspicion of acute coronary syndrome (ACS) at the ED between April 2016 and February 2020 were divided into 2 groups based on PET availability. The proportion of invasive coronary angiography (ICA) without significant coronary artery disease (CAD), length of stay (LoS), and additional downstream testing were evaluated. RESULTS: There were 21,242 ED visits for CP without definitive ACS: 5,492 when PET is not available and 15,750 when PET is available. When PET is available, proportion of patients undergoing a MPI study was greater (20.7% vs 17.6%, P<0.0001), proportion of ICA without significant CAD was similar (18.5% vs 21.4%, P=0.24), and median ED LoS was shorter (16.6 vs 18.1 hours, P=0.03). Patients undergoing SPECT MPI had significantly more downstream testing (8.9% vs 6.4%, P=0.003) and a higher rate of coronary angiogram without significant CAD (21.2% vs 14.2%, P=0.09) compared to those who underwent PET MPI. CONCLUSION: Availability of PET MPI was associated with an increased number of MPI referral from the ED, similar rates of ICA without significant CAD, decreased LoS, and fewer downstream testing.
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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 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".