Feasibility and repeatability of optical coherence tomography measurements of pre-stent thrombus burden in patients with STEMI treated with primary PCI
Bibliographic record
Abstract
AIMS: Intracoronary thrombus is disrupted by stent deployment and confounded by the development of intrastent plaque prolapse. This study aims at investigating the feasibility and repeatability of thrombus quantification prior to stent implantation using optical coherence tomography (OCT) in ST-segment elevation myocardial infarction (STEMI) patients. METHODS AND RESULTS: Patients were enrolled in an OCT substudy of the TOTAL trial (a randomized trial of routine aspiration ThrOmbecTomy with PCI vs. PCI ALone in patients with STEMI undergoing primary PCI) of aspiration thrombectomy or percutaneous coronary intervention (PCI) alone in primary PCI (PPCI) of STEMI. Using OCT, thrombus burden (TB) and other measures of pre- and post-stent atherothrombus were analysed over the arterial segment defined by the final stent length. Feasibility was 86.2% (25/29 patients) for both pre- and post-stent OCT imaging. Altogether, 8111 OCT cross-sections were analysed. In Bland-Altman analyses of inter-observer variation (n = 25), the mean pre-stent TB was 8.76% for Observer 1 and 8.97% for Observer 2 (limits of agreement -2.6 to 2.2%). In Bland-Altman analyses of intra-observer variation (n = 15), the mean pre-stent TB was 8.06% for the first and 7.35% for the second round of analysis by Observer 1 (limits of agreement -1.72 to 3.15%). There was a good correlation between pre-stent TB and pre-stent quadrants with thrombus/mm (r = 0.97), but only modest correlation between pre-stent TB and post-stent atherothrombotic burden (ATB; r = 0.54). CONCLUSIONS: Measurement of pre-stent TB by OCT during the PPCI of STEMI is feasible and highly repeatable. Pre-stent TB is only modestly correlated with post-stent ATB, which indicates that pre-stent measurements might be of additional value when assessing the TB in STEMI.
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".