Bibliographic record
Abstract
2nd ed, edited by Mary K. Gospodarowicz, Donald E. Henson, Robert V. P. Hutter, Brian O'Sullivan, Leslie H. Sobin, and Christian Wittekind, 809 pp, with illus, New York, NY, Wiley-Liss, 2001.Prognostic Factors in Cancer, second edition, is a concise and accessible compendium of essential information, abstracted from an enormous body of medical and scientific literature, about determinants of outcome in malignant disease. It is a distillation of the long-term efforts of the International Union Against Cancer to define those determinants from existing data and to put them into a framework that is relevant to clinical practice. Notwithstanding the incontrovertible data that extent of disease and histologic type of tumor are generally the most important indicators of outcome, broad overlap in outcomes of patients with tumors of like stage and type exist. Thus, the search for additional tumor-, site-, and patient-specific factors that can provide more accurate prediction of outcome and aid in clinical management of patients has been intense. This search has been fruitful but has generated an overwhelming volume of data. For the practicing pathologist assessing cancer specimens, such data are critically important, but the task of tracking and analyzing this rapidly expanding body of data is beyond the capabilities of most.This 800-page paperback volume organizes and summarizes current knowledge about prognostic factors in each major tumor site by topic and relevance. It does so in a manner that is straightforward and very user friendly, even for those completely uninitiated in this field. It also contains an excellent introductory section that deals with general principles of prognostic factor assessment, classification, measurement, and statistical analysis and of application to clinical decision-making and research.The site-specific sections cover the entire range of major cancer sites, including the brain, as well as cancer types, including both solid and liquid tumors. Concise epidemiologic summaries are provided. All relevant tumor-related and host-related factors are discussed and are organized in context of the strength of existing data. References are comprehensive and cited in the text. All chapters are heavily supplemented with helpful graphic presentations of information, and each ends with an appendix of summary tables that provide an at-a-glance summary of the factors discussed, stratifying them into 3 levels of import of validation: essential, additional, and new and promising. The book ends with a glossary of terms, common and specialized, that are frequently used (and not infrequently used incorrectly) in the field of prognostic factors.In summary, this book is a must-have for pathologists involved either in cancer diagnosis or cancer research. The multifaceted, multidisciplinary approach and the concise but comprehensive organization of Prognostic Factors in Cancer make it uniquely valuable to pathologists involved in the care of cancer patients. The book begins with a quote from Sir William Osler, once the chairman of the pathology department at McGill University: “Medicine is a science of uncertainty and an art of probability.” These words are especially applicable to cancer medicine, but it is fair to say that Prognostic Factors in Cancer does its part to help decrease the uncertainty.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.039 |
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".