Relationship between initial treatment strategy and quality of life in patients with coronary chronic total occlusions
Bibliographic record
Abstract
AIMS: Our objective was to evaluate the relationship between coronary chronic total occlusion (CTO) treatment strategy and quality of life improvements. METHODS AND RESULTS: This multicentre prospective cohort study enrolled consecutive CTO patients undergoing a non-urgent coronary angiogram who completed the Seattle Angina Questionnaire (SAQ) and EQ-5D at baseline and at one year. Strategies were: i) medical therapy, ii) PCI to non-CTO, iii) PCI to CTO, and iv) CABG. Multivariable regression models compared quality of life changes over time among strategies, accounting for repeat measures per patient. In our cohort of 387 patients, 154 underwent medical therapy, 83 had PCI to the non-CTO artery, 104 underwent CABG, and 46 underwent PCI to the CTO. Medically treated patients had no improvement on any SAQ domains. Patients with revascularisation of the CTO territory with either PCI or CABG had significant improvements in the physical limitation (PCI to CTO 60.5-76.4; CABG 61.6-80.1; p<0.001), angina frequency (PCI to CTO 79.0-92.7; CABG 82.1-97.9; p<0.001), and disease perception (PCI to CTO 50.5-75.0; CABG 50.2-80.0; p<0.001) domains. In non-CTO PCI patients, improvement was restricted to the angina frequency (82.8-93.3; p<0.001), and disease perception (53.8-71.4; p<0.001) domains. CONCLUSIONS: CTO territory revascularisation was associated with quality of life improvements.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".