PP17 Comprehensive Evaluation Of A Technology With Expanding Indications
Bibliographic record
Abstract
Introduction: The use of transcatheter aortic valve implantation (TAVI) is evolving. Our Cardiovascular Evaluation Unit is implementing a comprehensive approach to inform decision-makers on optimal use of TAVI, including the development of quality standards. We are implementing a multifaceted evaluation framework in collaboration with clinical stakeholders. Methods: Our unit has carried out a continuous field evaluation in collaboration with the clinical teams at all six TAVI centers in Québec for the past four years (1 April 2013–31 March 2017), with regular feedback to the teams and sharing of results with each individual center. Hospital documentation was reviewed according to established national quality indicator definitions. Field evaluation data were combined with the results of systematic literature review to establish provincial standards for practice, through a deliberation process by an interdisciplinary committee of clinical experts from each center. Systematic surveillance of the literature is ongoing. Results: In the period 2013–2017, use of TAVI in Québec was limited to very elderly patients with significant comorbidities at high risk of operative mortality. We observed improvements in both processes of care (e.g. documentation of risk scores) and clinical outcomes (e.g. 30-day and 1-year mortality) over time. Our consensus standards recognize the potential value of TAVI for patients at moderate operative risk, identify uncertainties and recommend best practices for patient evaluation and clinical decision-making about choice of treatment. Conclusions: A comprehensive, long-term evaluation process of TAVI with feedback to centers is associated with improvements in processes of care and outcomes. In the present context of expanding clinical indications, we will continue to evaluate patient selection, processes and outcomes according to the newly-established provincial quality standards. This iterative approach facilitates continued evidence generation and decision-making for optimal use of an evolving intervention. We acknowledge the contribution of the members of the expert clinical committee.
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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 | 0.087 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".