Bibliographic record
Abstract
Diagnosis, prognosis, and treatment are the three core elements of the art of medicine. Modern medicine pays more attention to diagnosis and treatment but prognosis has been a part of the practice of medicine much longer than diagnosis. Cancer is a heterogeneous group of disease characterized by growth, invasion and metastasis. To plan the management of an individual cancer patient, the fundamental knowledge base includes the site of origin of the cancer, its morphologic type, and the prognostic factors specific to that particular patient and cancer. Most prognostic factors literature describes those factors that directly relate to the tumor itself. However, many other factors, not directly related to the tumor, also affect the outcome. To comprehensively represent these factors we propose three broad groupings of prognostic factors: 'tumor'-related prognostic factors, 'host'-related prognostic factors, and 'environment'-related prognostic factors. Some prognostic factors are essential to decisions about the goals and choice treatment, while others are less relevant for these purposes. To guide the use of various prognostic factors we have proposed a grouping of factors based on their relevance in everyday practice; these comprise 'essential,' 'additional,' and 'new and promising factors.' The availability of a comprehensive classification of prognostic factors assures an ordered and deliberate approach to the subject and provide safeguard against skewed approaches that may ignore large parts of the field. The current attention to tumor factors has diminished the importance of 'patient' (i.e., 'host'), and almost completely overshadows the importance of the 'environment'. This ignores the fact that the latter presents the greatest potential for immediate impact. The acceptance of a generic prognostic factor classification would facilitate communication and education about this most important subject in oncology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".