Bibliographic record
Abstract
OBJECTIVES: Since 1988, four government-funded health technology assessment (HTA) agencies have been established in Canada. This paper is a descriptive review of reports issued by these organizations during the period from 1988 to 1998. METHODS: Publications from the national and three provincial HTA agencies in Canada were obtained and reviewed. Only the 117 assessment reports that were reported to have undergone external review were included in this analysis. Each report was classified on a standard abstraction form according to criteria such as technology type(s), assessment focus, whether a specific policy question was clearly stated and relevant decision maker(s) identified, description of search strategy and selection criteria, sources of data and assessment methods used, whether recommendations or conclusions were made, and duplication or overlap of reports. The trends in these qualities over the 10-year period were also examined. RESULTS: Therapeutic technologies have received the most attention from all four agencies, although the focus on devices, drugs, and procedures varied between agencies. The policy question under investigation was clearly identified in 82% of reports, and 71% clearly identified the decision maker toward whom the assessment was targeted. Efficacy or effectiveness was examined in 81% of reports, and costs were assessed in 65% of studies. These were the two most frequently examined aspects. Almost all assessments were descriptive literature reviews; 9% included meta-analyses and 32% had cost analyses or economic evaluations. Forty-four percent of reports had a clear description of the literature search strategy, and selection criteria were clearly specified in 38% of studies, but there was considerable variation among agencies in the level of description of these methods. Conclusions were clearly stated in 83% of the assessments' conclusions, and 13% had recommendations. When analyzed longitudinally, it is apparent that the quality of reports has improved markedly during the past decade. This was determined by examining the clarity of specifying the policy question(s) under investigation, the identification of the target audience of decision makers for the information, and by evaluating the thoroughness of the description of the methods used in the assessment. CONCLUSIONS: Canadian government agencies have contributed a considerable quantity of health technology assessments. There has been very little duplication of technologies evaluated, and the quality of the assessment reports has markedly improved during the past decade.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".