Tumor, Host, and Environment‐related Prognostic Factors
Bibliographic record
Abstract
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".