A commentary on the “GERABEL study” – Evaluating curative radiotherapy regimens guided by oncogeriatric assessment
Bibliographic record
Abstract
The GERABEL study [1], conducted by Daguenet and colleagues, aimed to evaluate the impact of curative radiotherapy (RT) regimens guided by oncogeriatric assessment on compliance and tolerance in breast cancer patients aged 70 years and older. This research represents an important step towards personalized medicine in geriatric oncology, as it utilizes a comprehensive oncogeriatric assessment to tailor RT regimens. By focusing on breast cancer patients over 70, the study addresses a significant gap in oncology research, as older adults are frequently underrepresented in clinical trials.
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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 | 0.037 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.065 | 0.066 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".