International percutaneous coronary intervention complication survey
Bibliographic record
Abstract
OBJECTIVES: To investigate the perceptions of interventional cardiologists (IC) regarding the frequency, impact, and management strategies of percutaneous coronary intervention (PCI) complications. BACKGROUND: The perceptions and management strategies of ICs of PCI complications have received limited study. METHODS: Online survey on PCI complications: 46 questions were distributed via email lists and Twitter to ICs. RESULTS: Of 11,663 contacts, 821 responded (7% response rate): 60% were from the United States and the median age was 46-50 years. Annual PCI case numbers were <100 (26%), 100-199 (37%), 200-299 (21%), and ≥300 (16%); 42% do not perform structural interventions, others reported performing <40 (30%), or >100 (11%) structural cases annually. On a scale of 0-10, participating ICs were highly concerned about potential complications with a median score of 7.2 (interquartile range: 5.0-8.7). The most feared complication was death (39%), followed by coronary perforation (26%) and stroke (9%). Covered stents were never deployed by 21%, and 32% deployed at least one during the past year; 79% have never used fat to seal perforations; 64% have never used coils for perforations. Complications were attributed to higher patient/angiographic complexity by 68% and seen as opportunities for improvement by 70%; 97% of participants were interested in learning more about the management of PCI complications. The most useful learning methods were meetings (66%), webinars (48%), YouTube (32%), and Twitter (29%). CONCLUSION: ICs who participated in the survey are highly concerned about complications. Following complication management algorithms and having access to more experienced operators might alleviate stress and optimize patient outcomes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".