IMPORTANCE OF CONTEXTUAL DATA IN PRODUCING HEALTH TECHNOLOGY ASSESSMENT RECOMMENDATIONS: A CASE STUDY
Bibliographic record
Abstract
OBJECTIVES: Contextual data and local expertise are important sources of data that cannot be ignored in hospital-based health technology assessment (HTA) processes. Despite a lack of or unconvincing evidence in the scientific literature, technology can be recommended in a given context. We illustrate this using a case study regarding biplane angiography for vascular neurointervention. METHODS: A systematic literature review was conducted, along with an analysis of the context in our setting. The outcomes of interest were radiation doses, clinical complications, procedure times, purchase cost, impact on teaching program, the confidence of clinicians in the technology, quality of care, accessibility, and the volume of activity. A committee comprising managers, clinical experts, physicians, physicists and HTA experts was created to produce a recommendation regarding biplane technology acquisition to replace a monoplane device. RESULTS: The systematic literature review yielded nine eligible articles for analysis. Despite a very low level of evidence in the literature, the biplane system appears to reduce ionizing radiation and medical complications, as well as shorten procedure time. Contextual data indicated that the biplane system could improve operator confidence, which could translate into reduced risk, particularly for complex procedures. In addition, the biplane system can support our institution in its advanced procedures teaching program. CONCLUSIONS: Given the advantages provided by the biplane technology in our setting, the committee has recommended its acquisition. Contextual data were of utmost importance in this recommendation. Moreover, this technology should be implemented alongside a responsibility to collect outcome data to optimize clinical protocol in the doses of ionizing delivered.
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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 | 0.258 | 0.442 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".