Tumor, Host, and Environment‐related Prognostic Factors
Notice bibliographique
Résumé
Abstract In the current climate of knowledge in oncology, an understanding of factors that govern outcome in cancer patients is essential to achieve the maximum therapeutic benefit. Discipline in the approach to patient management allows the most advantageous application of current evidence and leads to best practice. To determine prognosis it is helpful to considertumor‐relatedprognostic factors that characterize the disease,host‐relatedprognostic factors that typify the patient, andenvironment‐relatedfactors, which are external factors not directly related to either the disease or the patient. In this chapter, we review the most common factors in each of these categories. The discussion will focus on prognostic factors that are relevant at the time of diagnosis and initial treatment. However we caution that, in the management of a cancer patient, determination of prognosis could be required repeatedly to take account of multiple episodes throughout the course of the disease. Frequently these situations reflect decision‐making points (e.g., concerning adjuvant therapy, management of recurrent cancer, and palliative or terminal care). The diversity and impact of prognostic factors in each category will be demonstrated using examples from the recent literature. A comprehensive review of prognostic factors in distinct cancers follows in Part B of the book. Most cancer literature equates prognosis with tumor characteristics. Cancer pathology and anatomic disease extent account for most variations in cancer outcome. Since screening and early detection allow for diagnosis of small localized cancers, however, environmental factors are likely to have a more pronounced impact on outcome. This is particularly so as treatment becomes more effective. In addition, these factors are easier to influence by applying currently existing knowledge compared to those factors at the host or tumor level that are ingrained permanently in the phenotype of the case. The environmental factors are also the least studied despite evidence that they may have profound impact on outcome. For example, we reviewed Medline®publications between December 1, 1998 and November 30, 1999 under the search terms “prognostic factors in cancer” and “human” and “English language.” This revealed 21 of 983 papers on prognostic factors in cancer dealing with environment‐related factors, as indicated by their titles. In contrast, the same search yielded 189 publications with titles focusing on genetic or molecular prognostic factors. It would seem from this initial and potentially superficial examination that a disproportionate neglect of prognostic factors other than those related to the tumor might exist. This warrants further evaluation, since it is possible that an alternative situation is that the nontumor factors are either too few in number, not relevant to patient outcome, or will soon be surpassed by an overwhelming influence of new factors that are being detected with modern techniques. We believe that the latter is unlikely in the foreseeable future and that all of these factors are highly relevant to the task of uncovering prognosis in cancer.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».