Bibliographic record
Abstract
The objectives of this research are: to determine which hospitals are not performing TAVR as often as SAVR, and why? And to develop strategies/interventions to promote the use of TAVR. The research question for this study is: In Hospitals where alternative interventions are used more frequently than TAVR, can the understanding the decision-making behind choosing SAVR over TAVR help to develop a better uptake (implementation) strategy? The study design will consist of focus group interviews. Phase one will involve a series of four to five focus group interviews of a select respondent of eligible physicians recruited through the survey. The student principal investigator will conduct the interview, and each focus group will consist of five eligible participants. The entire process- inclusive of obtaining consent and the focus group interview will last 30-45 minutes each. Phase two will involve ideation and a co-creation session to develop strategies to address the barriers identified in phase one of the study. In this phase, the approach developed will then be tested amongst the stakeholders. The interview/focus group will be audio-recorded and transcribed for analysis by the research team. We employed the Toronto Translational Thinking Framework to ensure the research design is patient-centered. We hope this research will contribute to the understanding of the dissemination, implementation, and adoption of new standards of care. It will provide insights into the development and implementation of possible interventions to expedite the adoption of new practices, and improve the rate of TAVR over SAVR in Ontario- to lower the risk of complications for high-risk patients with severe aortic stenosis.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".