Tailored chemotherapy based on tumour gene expression analysis: breast cancer patients' misinterpretations and positive attitudes
Bibliographic record
Abstract
PELLEGRINI I., RAPTI M., EXTRA J.-M., PETRI-CAL A., APOSTOLIDIS T., FERRERO J.-M., BACHELOT T., VIENS P., JULIAN-REYNIER C. & BERTUCCI F. (2011) European Journal of Cancer Care21, 242–250 Tailored chemotherapy based on tumour gene expression analysis: breast cancer patients' misinterpretations and positive attitudes The aim of this study was to document how breast cancer patients perceive their prognosis and a tailored treatment based on tumour gene expression analysis, and to identify the features of this approach that may impact its clinical application. In-depth interviews were conducted at three French cancer centres with 37 women (35–69 years of age) with node-positive breast cancer undergoing an adjuvant chemotherapy regimen defined on the basis of the genomic signature predicting the outcome after chemotherapy. Several concerns were identified. First, some misconceptions about these methods were identified due to semantic confusions between the terms ‘genomic’ and ‘genetic’, which generated anxiety and uncertainty about the future. Second, the ‘not done’ and ‘not interpretable’ signatures were misinterpreted by the women and associated with highly negative connotations. However, the use of tumour genomic analysis to adapt the treatment to each patient received most of the patients' approval because it was perceived as an approach facilitating personalised medicine. In conclusion, improving the quality of provider/patient communications should enable patients to play a more active part in the decision making about their treatment. This will ensure that those who agree to have tumour gene analysis have realistic expectations and sound deductions about the final result disclosure process.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".