Notice bibliographique
Résumé
Abstract The activities of clinical practice of medicine include the processes of diagnosis, treatment, and follow‐up care. Interspersed throughout is the fundamental activity of prognostication. Whatever the situation, physicians are asked daily about the foreseeable outcome of the disease, expected results of treatment, and possible complications. The care of patients with cancer involves a series of steps, starting with the initial assessment, leading to the diagnosis, treatment, and assessment of outcomes. In each of these steps, and in all forms of physician–patient interaction, the ability to communicate the prognosis or to predict the probable outcome is critical. The modern approach to patient management endorses clinical practice based on scientific evidence from experiments or observations. To facilitate consistent management, and to facilitate audit; evidence‐ or consensus‐based clinical practice guidelines are developed for patient groupings based according to defined and reproducible characteristics and reliable predictions of different outcomes. The necessity of grouping patients with similar characteristics to guide treatment and to anticipate the outcome has been recognized as far back as the seventeenth century. The development of a prognostic classification for infections was followed by classifications for other diseases. In cancer, a formal staging classification (the TNM system) has been in use for over 50 years. Cancer presents a formidable challenge for classification because it comprises a very heterogeneous group of diseases. The fundamental elements required to characterize each cancer are the organ of origin, the histologic type, and in addition numerous prognostic factors that characterize the tumor, the patient, and the environment surrounding the patient. Knowledge of prognostic factors is essential to all aspects of cancer care. Beginning with the diagnosis, and extending through the process of treatment planning, outcome assessment, and planning of support measures, it is essential to be familiar with issues that concern prognosis. Moreover, the knowledge, familiarity, and comprehension of this information are necessary to communicate with patients and their caregivers. Well‐informed patients are better equipped to face the future and become partners in our efforts to improve outcomes through the generation of new knowledge through participation in clinical research in an informed manner. In the process of diagnosis, the knowledge of factors that discriminate for more advanced disease presentations helps to reduce the need for unnecessary tests, while knowledge of the likely failure pattern leads to site‐specific tests to rule out metastasis. For example, a low prostatic‐specific antigen (PSA) level predicts for the presence of localized prostate cancer and obviates the need for extensive staging investigations. In the process of understanding prognosis, a compilation of prognostic factors is analyzed to predict the future outcome. The international consensus on prognostic factor classifications in non‐Hodgkin's lymphoma and germ‐cell testis tumors are examples of wide use of multiple prognostic factors in the decision making and outcome assessment of these tumors.
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 | 0,005 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 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 ».