Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to describe an initial exploration by CADTH, Canada's pan-Canadian health technology assessment (HTA) agency, in using the INTEGRATE-HTA guidance in the production of an HTA that examined the use of both in-center and in-home dialysis modalities for the treatment of end-stage kidney disease in adults in Canada. METHODS AND RESULTS: We outline CADTH's standard HTA production process and context and then describe the experience of the assessment team in using the INTEGRATE-HTA guidance, specifically to help structure and guide the use of a logic model, the identification of implementation issues, and the identification and examination of ethical issues. For each of the aspects, we describe and reflect on how the assessment team used the guidance, challenges that were encountered in its use, and whether and how we might address these challenges when using the INTEGRATE-HTA guidance in the future. CONCLUSIONS: INTEGRATE-HTA provided detailed and helpful guidance for truly integrating wide-ranging aspects of HTA. Our agency was challenged by a steep learning curve for assessment team members, tight project timelines, and a misalignment of current HTA processes with those required to implement the guidance. Nevertheless, using the guidance initiated a dialogue about what might be needed to assess complex interventions and the potential process changes that could facilitate conducting more integrated assessments.
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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 | 0.056 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| 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".