Bibliographic record
Abstract
Off-label prescribing is particularly common in oncology. While it brings abundant benefits to cancer treatment, decisions on off-label prescribing should be made with caution, due to insufficient supporting data, weak safety monitoring system, and increased health care burden. Currently, reimbursement decisions for off-label oncology are based on recommendations from four drug compendia, each of which combines data from clinical trials and/or observational studies and expert opinions. Further enhancements are expected in terms of transparency and consistency of compendia's methods of data synthesis. While the existing FDA regulations prohibit direct-to-prescriber promotion, with the exception of publication on off-label drug use, considerable leeway may be given to late-stage cancer patients. Clinical Trials for oncology off-label indication should focus on late stage cancer patients beyond first-line therapy and patient sample should have equal representations from academic and community settings. Off-label oncology clinical trials should also provide full information on conflict of interest. Given the high stakes involved in oncology treatment, future policies should strike a balance between innovation and clinical, economic, and humanistic consequences.
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 | 0.107 | 0.278 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".