Oncology: What You May Have Missed in 2024
Bibliographic record
Abstract
Over the past 5 decades, substantial advances in oncology have reshaped cancer care, reflecting the dynamic role of internal medicine physicians in patients' journey from screening to diagnosis, treatment, and surveillance. This review highlights 10 landmark studies from 2024 that address emerging therapies and evolving clinical standards. Immunotherapy remains a central focus, with checkpoint inhibitors redefining the management of solid tumors and showing expanded applications across disease sites and earlier stages of disease. Targeted therapies and antibody-drug conjugates, including trastuzumab deruxtecan and enfortumab vedotin, are enhancing precision treatment options in metastatic cancer. Meanwhile, advances in supportive care, such as magnetic resonance imaging-guided prostate cancer screening, ponsegromab for cachexia, and celiac plexus radiosurgery for pain, show enhanced symptom management and quality of life for patients. These innovations highlight the critical role of multidisciplinary approaches, where internal medicine physicians contribute to co-management and toxicity monitoring, ultimately optimizing patient care. By staying current with these developments, internal medicine physicians are positioned to navigate complex oncologic care, ensuring that the benefits of novel therapies are maximized while mitigating their challenges.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.043 | 0.024 |
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".