Oncology: What You May Have Missed in 2023
Bibliographic record
Abstract
Advances in oncology treatment methods have improved outcomes and quality of life for patients with cancer. However, care of these patients can be complex, and the contribution of physicians from different specialties is crucial. This article highlights important publications from 2023 on topics across a wide spectrum relating to the management of oncology patients. The literature was screened for significant new evidence that is relevant to internal medicine specialists and subspecialists whose focus is not oncology. Two articles address the importance of social interventions targeting end-of-life care for low-income and minority patients and the well-being of caregivers. Two additional articles address screening considerations in patients at risk for colorectal and lung cancer. Two more articles address safe use of hormone-related therapies to treat symptoms of menopause and prevent disease recurrence or progression in patients diagnosed with noninvasive breast neoplasia. Finally, several articles were included on topics related to COVID-19 vaccination in patients with cancer, use of cannabinoids for cancer pain control, chronic autoimmune adverse effects related to use of immune checkpoint inhibitors, and the incidence of second primary neoplasms.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.109 | 0.054 |
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".