Bibliographic record
Abstract
By all accounts, the past several years has seen palliative care leap into the nation’s health care headlines and perhaps its consciousness as never before. Many point to 2010 as a turning point, when Massachusetts General Hospital oncologist Jennifer Temel, M.D., reported a landmark study in the New England Journal of Medicine showing that terminal lung cancer patients given palliative care at the time of diagnosis, along with curative cancer treatment, not only had a better quality of life but also lived a median of 3 months longer. And in 2012, the American Society of Clinical Oncology (ASCO) released a report concluding that according to available evidence, all patients with metastatic cancer can receive palliative care—defined as symptom and pain management, and psychosocial and other supportive care aimed at improving quality of life—at the time of diagnosis. “All of this interest in palliative care is related to the fact that more people are living with cancer than ever before,” said medical oncologist Edith Mitchell, M.D., professor of medical oncology at Jefferson Medical College of Thomas Jefferson University in Philadelphia. “Years ago, we had fewer drugs, and patients didn’t live as long, and fewer survived cancer. Now patients live longer and there are short- and long-term effects of therapy. With newer drugs and [toxic effects], patients may be staying on treatments longer, and symptom management has become a major part of what we do. It is important for the oncologist to know about supportive care and how it really fits in with other care for the patient.”
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 | 0.058 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.021 |
| Scholarly communication | 0.019 | 0.030 |
| Open science | 0.006 | 0.034 |
| Research integrity | 0.015 | 0.040 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".