Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,036 | 0,039 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».