Measuring the Accuracy of Prognostic Judgments
Notice bibliographique
Résumé
Abstract The practical test of a true science is the power it confers of prevision, or of knowing now what will follow hereafter. When we can prognosticate with certainty, medicine will have become a science. —H. Hartshorne A system of medicine Prognosis can be defined as, “a forecasting of the probable course and termination of an illness.” In the clinical practice of oncology the prognosis is important because it may determine the optimal choice of treatment, and because patients often want to know what is going to happen to them in order to prepare themselves for the future. Given that prognostic judgments are used as a basis for important decisions, we need to know how accurate they are. We may not yet be able to achieve the “certainty” in prognosis that Hartshorne envisaged as a characteristic of the scientific medicine, but we should at least be able to measure the predictive value of our prognostic judgments, and take this into account in our decision making. It is generally accepted today that we should not place reliance on the results of diagnostic tests without knowing their accuracy, and the same principle should apply to prognostic judgments. However, while modern textbooks of clinical epidemiology deal thoroughly with methods for measuring the accuracy of diagnostic tests, much less has been written about how to measure the accuracy of prognosis. Although there is a very extensive literature about prognostic factors in cancer, there have been very few reports relating to the accuracy of prognostic judgments in practice in individual cases. In this chapter, we are concerned mainly with the quality of prognostic judgments at the level of the individual patient. This is much more of a challenge than predicting the outcome of groups of cases. Once the average outcome of a specific medical problem has been established in a large group of cases, the average outcome in another large group of similar cases may be predicted with great precision. However, a precise knowledge of the average outcome of an illness may have little or no predictive value in the individual case. For example, if a large group of patients with a specific stage of a specific cancer has been observed to have a 5‐year survival of 50% then we can be confident that if a large group of similar cases is managed in the same way in future, it will also have a 5‐year survival of about 50%. When we try to use this information to predict the outcome in the next case of this illness that we encounter, however, it translates into a probability of 5‐year survival of 0.5; In other words, it leaves us in a state of complete uncertainty. The main objectives of this chapter are to describe methods that can be used to measure the accuracy of prognosis at the level of the individual case, and to review what little is known about the accuracy of prognostic judgments in oncology. We end by considering factors that may limit the degree of “certainty” that we can ever expect to achieve in prognosis in oncology.
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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 | 0,029 | 0,348 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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 ».