Perfusionist removal of intra-aortic balloon pump catheters improves efficiency without an increase in complication rates
Bibliographic record
Abstract
Introduction The intra-aortic balloon pump (IABP) is one of the most utilized cardiac assist devices. Patients receiving IABP therapy are typically managed in high acuity clinical care areas with limited bed space and high demand. Our center instituted a certified clinical perfusionist (CCP) led initiative to remove IABP catheters in order to reduce IABP therapy time, hasten removal and improve efficiency. Methods The purpose of the study is to compare outcomes for IABP removal by a certified clinical perfusionist to a physician. The primary outcome measures were site hematoma score and limb related complications. A survey was submitted to bedside nurses, managers/patient care coordinators, CCP’s and physicians. The IABP quality assurance database was interrogated for the study. Results There were 350 patients eligible for inclusion. The cohort was well balanced between CCP ( n = 284) and physician ( n = 66) groups for patient demographics, indication, insertion specifics and type of medical intervention. The majority of patients had no bruise or hematoma with perfusionist ( n = 246, 87%) or physician ( n = 58, 88%) ( p = 0.78) removal. The physician group demonstrated a higher rate of grade 3 hematomas ( p = 0.03). There was no statistically significant difference between CCP and physician groups for limb complications and mortality. Survey results showed an improved efficiency in bed space allocation, physician workload and a decreased IABP support time. Conclusion There is no difference in limb complications between perfusionist and physician removal of IABP catheters. The survey demonstrate an improvement in resource allocation and efficiency. A perfusionist led IABP removal program can be done safely and may help improve program efficiency.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.005 | 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".