Prognostic Factors: Principles and Applications
Bibliographic record
Abstract
Abstract Since the beginning of time, man has wanted to prognosticate, or “know before.” This desire explains the popularity of psychics and astrologers. After the birth of the idea of chance, prediction of the future has been largely handed over to statisticians. In studies of cancer and other diseases, identification of prognostic factors is the present‐day equivalent of predicting the future. Nonetheless, it would be implausible to believe that we can predict precisely for the individual patient. In reality, all we can provide are statements of probability, and even these are more accurate for groups of patients, the study of whom provides us with our knowledge about prognosis. Furthermore, the practical management of cancer patients requires us to make predictions and decisions for individuals, and the challenge of prognostication is to link the individual patient to the collective population of patients with the same disease. In this chapter we deal with the rationale for prognostic factors and describe classifications of these factors with attention to those used in this book. We also discuss potential endpoints relevant to oncology, the taxonomy of prognostic factors, and their applications in practice. Most importantly, we introduce a concept of a management scenario that forms the basis for defining prognosis at a given point in the course of disease.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".