A decision support system for stress only myocardial perfusion scintigraphy may save unnecessary rest studies
Bibliographic record
Abstract
Aim: It is often a practical question whether to continue with the rest study after termination of a fairly normal stress myocardial perfusion scintigraphy (MPS), in particular for physicians with limited experience. The purpose of this study was to analyze the value of using a decision support system (DSS) to guide less experienced physicians in this situation. Methods: Nine residents from eight different nuclear medicine departments interpreted 100 MPS stress studies, first without and then with access to the advice of a DSS. Each study was interpreted regarding the necessity of adding a rest study for correct interpretation of the MPS. The patients had undergone a gated stress and rest MPS, using a Tc-99m sestamibi protocol. Interpretations made by three nuclear cardiology experts, having access to all available clinical and image information, were used as the gold standard. Results: In the cases where the gold standard interpretation wanted a rest study the 9 residents asked for it in 94% and 95% before and after having access to the DSS, respectively ( p >0.05). The residents did not want a rest study in 57% (without) and 69% (with the advice from the decision support system), in the patients, considered to have a normal stress study by the experts ( p <0.005). The DSS significantly reduced interobserver variation among the residents. Conclusion: The present study shows that with the support of a DSS less experienced physicians get closer to the decisions of highly experienced nuclear cardiologists regarding the need of adding a rest study to a stress MPS.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".