Comparing the image quality of myocardial perfusion agents in the clinical laboratory: small test groups and large reference populations
Bibliographic record
Abstract
Myocardial perfusion studies have been performed for 6 years using technetium-99m (99Tcm)-sestamibi (MIBI). In this study we evaluated a newer agent, 99Tcm-tetrofosmin (TF), on a trial basis for 2 weeks, and compared the results from each week to those in an adjacent week of MIBI use. The routine protocol included weight-based dosing, frequent dipyridamole use, and separate-day rest and stress wherever possible. During the first week, TF was used with 'usual' image timing, i.e. stress tomography performed 30-60 min after stress, and ancillary immediate images performed 4 min after injection. For the second week, 'early' tomography was performed 15-30 min after stress. TF scans (n = 53) were compared with MIBI scans for the adjacent weeks (n = 54) and with a historical reference series (n = 1800). Blinded analysis was made of tomographic image quality, peak myocardial counts and background activity (lung and abdomen) on immediate and delayed acquisitions and on tomographic reconstructions. The TF and MIBI test groups were similar with respect to gender, weight, stress protocol, tracer doses, imaging times and scintigraphic findings. Using analysis of variance, the tomographic quality scores were similar for the two observers, with stress>rest (P<0.0001), 'usual'>'early' (P<0.001) and MIBI>TF (P<0.05). Myocardial counts were approximately 20% higher with the MIBI test group than with TF at all times after stress (P=0.001), and were similar to the reference population. MIBI with usual timing gave more favourable stress abdominal background ratios than the other three agent/timing combinations. Satisfactory images could be obtained with TF, but no apparent advantage over MIBI could be attained with earlier post-stress imaging. Subtle advantages for MIBI over TF were suggested by comparison of the small test groups. In our local imaging context, these conclusions were reinforced by a large control series.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".