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.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".