Prognostic value of automated quantification of 99mTc-sestamibi myocardial perfusion imaging.
Bibliographic record
Abstract
UNLABELLED: Although human interpretation of (99m)Tc-sestamibi SPECT myocardial perfusion imaging has been repeatedly validated in the diagnostic and prognostic assessment of coronary artery disease, it remains unclear if automated computer-derived quantitative indices of perfusion have similar or independent prognostic information. METHODS: We studied 718 patients referred for (99m)Tc-sestamibi SPECT myocardial perfusion imaging who were followed for 5.6 +/- 1.1 y (mean +/- SD). The SPECT studies were initially interpreted visually without benefit of computer-based analysis and were then subjected to a blinded reprocessing to extract quantitative indices of perfusion. Follow-up was through the Manitoba Population Health Research Data Repository. Acute myocardial infarction or cardiac death occurred in 79 individuals (11.0% of the cohort). RESULTS: Visual and quantitative categorization of scan perfusion abnormalities showed similar prognostic value for predicting acute myocardial infarction or cardiac death. Discordance between the visual and quantitative categorizations defined a group at intermediate risk. There was a gradient of risk with increasing severity of the summed stress score (SSS) or summed difference score (SDS). The automated SSS and SDS provided incremental prognostic information over that obtained from visual interpretation. CONCLUSION: Automated quantification of (99m)Tc-sestamibi SPECT myocardial perfusion scans provides objective prognostic information and may complement the conventional visual image interpretation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".