Personalised medicine in Canada: a survey of adoption and practice in oncology, cardiology and family medicine
Bibliographic record
Abstract
Introduction In order to provide baseline data on genetic testing as a key element of personalised medicine (PM), Canadian physicians were surveyed to determine roles, perceptions and experiences in this area. The survey measured attitudes, practice, observed benefits and impacts, and barriers to adoption. Methods A self-administered survey was provided to Canadian oncologists, cardiologists and family physicians and responses were obtained online, by mail or by fax. The survey was designed to be exploratory. Data were compared across specialties and geography. Results The overall response rate was 8.3%. Of the respondents, 43%, 30% and 27% were family physicians, cardiologists and oncologists, respectively. A strong majority of respondents agreed that genetic testing and PM can have a positive impact on their practice; however, only 51% agreed that there is sufficient evidence to order such tests. A low percentage of respondents felt that they were sufficiently informed and confident practicing in this area, although many reported that genetic tests they have ordered have benefited their patients. Half of the respondents agreed that genetic tests that would be useful in their practice are not readily available. A lack of practice guidelines, limited provider knowledge and lack of evidence-based clinical information were cited as the main barriers to practice. Differences across provinces were observed for measures relating to access to testing and the state of practice. Differences across specialties were observed for the state of practice, reported benefits and access to testing. Conclusions Canadian physicians recognise the benefits of genetic testing and PM; however, they lack the education, information and support needed to practice effectively in this area. Variability in practice and access to testing across specialties and across Canada was observed. These results support a need for national strategies and resources to facilitate physician knowledge, training and practice in PM.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".