Physician Preferences for Bone Metastasis Drug Therapy in Canada
Bibliographic record
Abstract
BACKGROUND: Currently in Canada, several bone-targeted agents (btas) with varying characteristics are available for the prevention of skeletal-related events (sres) in patients with bone metastasis secondary to solid tumours. In the present study, we evaluated the preferences of physicians in Canada for the various attributes of the available btas. METHODS: Physicians treating patients with bone metastasis from solid tumours were invited to complete an online discrete-choice experiment. Respondents were asked to choose between pairs of hypothetical medications for virtual patients. Each hypothetical medication was described based on predefined key attributes: time until first sre, time until worsening of pain, medication-related annual risk of osteonecrosis of the jaw (onj), medication-related annual risk of renal impairment, and mode of administration. A random-parameters logit model was used to analyze the choices between hypothetical medications and thus infer physician preferences for medication attributes. RESULTS: Responses from the 200 physicians who completed the discrete-choice experiment suggested that months until first sre, risk of renal impairment, and months until worsening of pain were considered the most important attributes affecting choice of bta. The annual risk of onj was considered the least important attribute. CONCLUSIONS: When making treatment decisions about the choice of bta for patients with bone metastasis from solid tumours, delaying sres and worsening of pain, and reducing the risk of renal impairment are primary considerations for physicians in Canada.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".