Impact of a dedicated radial lounge on same‐day discharge percutaneous coronary intervention
Bibliographic record
Abstract
BACKGROUND: The same-day discharge (SDD) program after elective percutaneous coronary intervention (PCI) is a safe strategy that allows for the optimization of hospital resources. However, the lack of adequate infrastructure and a specially targeted care model may limit its implementation. Our center developed an outpatient care model based on an area designed for percutaneous procedures called radial lounge (RL). AIMS: Evaluate the efficacy and safety of the RL care model: (1) SDD rate, (2) patient experience, (3) major adverse cardiac events (MACEs) (in-hospital, 30-day, and 1-year mortality and intervention), and (4) vascular access complication. SECONDARY OBJECTIVE: Impact of RL SDD rate on total elective SDD-PCI volume. METHODS: We conducted a retrospective observational cohort study at a cardiovascular hospital, including consecutive patients undergoing elective PCI between 2015 and 2022 who were admitted to the conventional hospitalization area (CHA) or the RL about the stated objectives. Patient experience was assessed using the Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. RESULTS: A total of 5466 elective PCI procedures were included: 2102 in the RL and 3364 in the CHA. The SDD rate was 85.2% in the RL group and 54% in the CHA. After the implementation of RL, a significant increase in the volume of elective SDD-PCI was observed and patient satisfaction improved significantly (p < 0.005) with CHA. Finally, a greater amount of MACEs were not observed in the RL. CONCLUSIONS: The PCI program in RL proved to be safe and effective. It showed a higher rate of SDD and a significant improvement in patient experience was observed without affecting safety.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".