<i>It provides families with other avenues for treatment when there are no other options</i> Surgeons' perspectives of being part of a precision medicine trial for poor prognosis paediatric cancer patients: A short report
Bibliographic record
Abstract
OBJECTIVE: Precision medicine is transforming cancer treatment, yet the perspectives of surgeons who often play a critical role in the delivery of precision medicine remain understudied. METHODS: We conducted semi-structured interviews with 13 surgeons involved in a precision medicine trial for children with poor prognosis cancer. We explored knowledge of genetics, confidence with somatic and germline results, ratings of benefit to stakeholders and willingness to undertake surgical procedures. RESULTS: Surgeons generally had positive attitudes towards precision medicine but expressed concerns about families' unrealistic expectations, mixed opinions on the benefits and the use of research-only biopsies. Most surgeons rated their genetics knowledge as 'good' (69%) and felt 'very confident' in identifying genetic specialists (66%), but 'not confident' (66.6%) in making treatment recommendations. Surgeons' willingness to undertake a procedure was influenced by potential patient benefit. CONCLUSIONS: Our findings support the need for more workforce and training support for surgeons to fully engage with precision medicine.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".