Bibliographic record
Abstract
Some of the most exciting recent advances in cancer research have come from learning more about how diverse a disease “cancer” actually is. For example, genomic analysis has identified at least 10 subtypes of breast cancer, with implications for varying therapeutic approaches ( 1 ). Against this backdrop, the set of four papers in the current issue of the Journal take a seemingly opposite approach: seeking commonality in symptom assessment across cancers, with the aim of identifying a core set of symptoms for use in clinical trials ( 2–5 ). The four papers have slightly different emphases, all focusing on identifying core symptoms in: different cancers ( 2 ), prostate cancer ( 3 ), head and neck cancer ( 4 ), and ovarian cancer ( 5 ). The authors comprise some of the most respected researchers in the quality of life (QoL) and cancer field, and their desire to promote assessment of cancer-related symptoms in clinical trials is laudable.
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 | 0.097 | 0.362 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".