Bibliographic record
Abstract
Abstract [Our original introduction to Listen to the Patient is published again to emphasize the Journal's commitment to this important series.] For more than 50 years, Cancer has been devoted to exploring every facet of malignant neoplasia, from morphology to therapy, from epidemiology to biology, from historical perspective to basic science. One aspect, however, needs emphasis, namely, the human being who bears the burden of the cancer. For this reason and with the encouragement of the Editor, we have undertaken to remedy this situation by directing attention to the fears and frustrations, hopes and expectations, and feelings and thoughts of all kinds of the person who has cancer, who is worried about it, and who is dependent on physicians specialized in its diagnosis and management. For doctors to be understanding of the very deep needs of patients with cancer, it is requisite that they be privy to authentic, but often unspoken, expressions of a patient's anxieties about physical and emotional pain, loss of control over personal destiny, and plain dread of dying and of the end of cherished relationships. Barrie R. Cassileth, Ph.D. Integrative Medicine Service, Memorial Sloan‐Kettering Cancer Center, New York, New York. A. Bernard Ackerman, M.D. Ackerman Academy of Dermatopathology, New York, New York. Cancer 2003;97:2707–9. © 2003 American Cancer Society. DOI 10.1002/cncr.11404
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| opus | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.017 | 0.040 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".