083PS Incorporating Guidelines into Local Clinical Practice and Policy Through the Use of Practice-Based Health Technology Assessment
Bibliographic record
Abstract
Background Practice-based health technology assessment (PB-HTA) has the potential to improve the quality, safety and value of patient care by synthesising evidence to provide timely guidance for clinical practice, policy, formulary management, operations, and purchasing decisions. Hospital-based HTA centres are active in Western Europe and Canada, but less so in the US, and some operate in conjunction with formal evidence-based guideline programmes. Objectives/Goals To actively engage leaders of PB-HTA to discuss strengths and limitations, lessons learned, and the role of PB-HTA in the development, dissemination, and implementation of guidance within health care systems. Target Group/Suggested Audience Senior executives/administrators, and clinical policy, quality and safety leaders in healthcare organisations and networks who develop, implement and measure performance related to clinical guidance. Description of Aession and Speaker Topics Dr. Wyer, who leads a PB-HTA capacity building programme for health care organisations at the NYAM SEBHC, will engage the panellists in a discussion of their experiences leading PB-HTA efforts at Kaiser Permanente (Ms. Koster), the Veterans Administration (Dr. Helfand), Penn Medicine (Dr. Umscheid) and the HTA unit at the Catholic University Hospital in Rome, Italy (Dr. Marchetti). The discussion will address the potential for guidance developed by PB-HTA centres to impact the quality, safety and value of patient care, similarities and differences in national and international efforts, and future directions for the field.
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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 | 0.134 | 0.261 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.010 |
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".