Clinical usefulness of genetic testing for drug toxicity in cancer care: decision-makers’ framing, knowledge and perceptions
Bibliographic record
Abstract
To explore the clinical uptake of pharmacogenetic/pharmacogenomic toxicity testing to reduce adverse drug reaction incidences, this paper analyzes data collected through semi-structured face-to-face interviews with clinicians and/or clinician-scientists, primarily in the context of cancer treatment in multi-ethnic California (US), Vancouver (Canada) and Singapore. Recurrent themes in the data include the following: first, the scientific evidence for drug-gene interactions is perceived to be generally weak. Second, the primacy of medical treatment’s efficacy over toxicity is the predominant frame through which clinicians consider testing. Third, physicians tailor their decisions according to each patient’s tolerance levels for toxicity. Fourth, racially and ethnically based toxicity risk estimates are a factor shaping the clinical uptake of genetic tests, but they are controversial. These factors contribute to the low clinical uptake of toxicity testing for predictive purposes. We argue that the decision-makers’ framing and perception are additional features to be considered in Hedgecoe’s (2008) “clinical usefulness” framework.
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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 | 0.062 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".