Evidence-Based Approach to the Introduction of Positron Emission Tomography in Ontario, Canada
Bibliographic record
Abstract
PURPOSE: The uptake of new health care technologies is usually driven by industry promotion, physician interest, patient demand, and institutional ability to acquire the technology. The introduction of positron emission tomography (PET) scanning in the province of Ontario, Canada, followed a different path. METHODS: The Ontario provincial government, through its Ministry of Health and Long-Term Care, commissioned a systematic review of the literature. When this found only weak evidence that PET has a positive impact on clinical outcomes, the Ministry introduced a provincial PET evaluation program to close the evidence gap. RESULTS: This article describes the challenges encountered establishing the PET evaluation program. These included the design and conduct of the initial clinical trials, the establishment of a PET cancer registry, standardizing how PET scans were performed and reported, and gaining acceptance by health professionals for the evaluative program. CONCLUSION: The proliferation of health technologies is a key driver of increasing health care costs. The Ontario approach to the introduction of PET is a model worth consideration by health systems seeking to ensure that they receive value for money based on a strong evidentiary base when introducing new health technologies.
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 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 | 0.027 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".