Standardization of Central Off-Line Quantitative Image Analysis: Implications from Experiences with Quantitative Coronary Angiography
Bibliographic record
Abstract
Developments in both medical equipment and software provide the opportunity to obtain more detailed, and in many instances direct digital images, i.e. not using film or video, that can be used for off-line quantitative analysis. As a result, the use of imaging data besides clinical data as a primary end point in clinical trials has nowadays become more acceptable. However, whereas clinical laboratories have been standardized worldwide according to prespecified criteria, standardization of core laboratories conducting centralized off-line quantitative analysis is still lacking. Here, we describe the procedures and guidelines to be followed to standardize off-line analysis in quantitative coronary arteriography. By using a set of standard images and phantoms, the compliance of a core laboratory with respect to these requirements and the acceptable range of interobserver variability can be verified on a regular basis. This will improve the overall quality of a study and, even more importantly, the patient outcome.
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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 | 0.223 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".