Bibliographic record
Abstract
From the Editor What does Quality Mean for Perfusionists?Never before in history has there been more measurement of healthcare process and outcome variables.Today, all healthcare organizations are re-doubling their efforts to improve the quality of care by driving down the rate of costly errors, waste, and the misuse and overuse of care.So, exactly what does quality mean for the cardiovascular perfusionist?The aim of this Brief Editorial is to define quality in health care and provide a road map for cardiovascular perfusionist professionals to engage in the movement to improve quality.According to Carolyn Clancy, the Director of the Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services, ". . .health care quality is getting the right care to the right patient at the right time-every time (1)."Quality in health care has also been defined as "based on evidence."David Sackett has said that evidence-based medicine is "the conscientious, explicit and judicious use of current best evidence in making decisions about the care of the individual patient.It means integrating individual clinical expertise with the best available external clinical evidence from systematic research (2)."From this definition, it is implicit that the external scientific evidence must become integrated closely with local practice and serve as the underpinning of how care is delivered to each patient.Jeremy Grimshaw, of the Ottawa Health Research Institute, described the study of the uptake of research findings as Implementation Research (3).Implementation Research focuses on learning how to change the technical aspects of care into a system that is evidencebased at the local level.These statements suggest that quality is related to both the science and the delivery of care.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".