Prognosis and Classification of Cancer
Bibliographic record
Abstract
Summary Even in cancers where a substantial proportion of patients have a successful outcome, not all are cured. There is a need to address these disparities scientifically under a broad rubric that encompasses prognostic factor research. Within this domain exists an interdependent array of principles, definitions and data underpinning the complex nature of oncology outcome. These address clinical, biological, interventional and diagnostic dimensions that attempt to understand the reasons for treatment failure or success in individual or groups of patients. This field evolves continuously and can be expected to change with new knowledge and concepts. At the same time, systematic analysis, reporting and comparison of results requires adherence to agreed standards for nomenclature, taxonomy and classification. At present, no uniformly agreed classification exists for prognostic factors. A description of a framework proposed by the UICC is described to address clinical relevance in this evolving field that acknowledges numerous dimensions of factors related to the patient, tumour, and healthcare system, and is used in the disease‐site chapters of this Manual. Future enhancements can be anticipated through advances in protocols, standards, classification and terminology, combined with a general understanding of the needs for interpretation and implementation.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".