Global Consensus on Prognosis and Outcomes in Cancer
Bibliographic record
Abstract
Background and context: Prognosis in cancer implies a probability of an outcome, but many factors need consideration to address this: disease type and molecular characteristics, anatomic disease extent (i.e., stage), patient characteristics and comorbidities, and any intervention (treatment) applied. A number of these parameters should ideally be collected by cancer registries as part of an effective cancer control program to promote appropriate care and cancer program planning. The UICC TNM Prognostic Factors Project team has classified prognostic factors according to the context of host, tumor, and environment. These prognostic factors should also be considered within the time setting of a patient's disease (i.e., a specific treatment scenario) and according to what outcome is being predicted. This approach recognizes that there are many different outcomes in oncology, not just survival. These principles need to be understood to avoid confusion in interpreting data and outcomes research. Aim: Consensus is required to understand and support the multifaceted challenges of prognostication, including the means for estimating prognosis, support efforts to standardize recording of factors that effect outcomes, bring consistency in defining paradigms (settings/scenarios) within which to address outcomes, and examine mathematical digital/AI tools for decision support. Strategy/Tactics: In April 2018 the UICC TNM Prognostic Factors Project is holding a global consensus meeting involving key organizations involved in cancer prognosis and cancer registries to discuss these challenges. It will emphasize the value of prognosis in cancer, factors affecting prognosis relevant to the global cancer community, and the importance of outcomes and their applicability to “value based care.” Program/Policy process: It will emphasize the value of prognosis in cancer, factors affecting prognosis relevant to the global cancer community, and the importance of outcomes and their applicability to “value based care.” Outcomes: A commentary and more detailed paper outlining the purpose, results of the discussion, and next steps in implementation will be drafted following the consultation. What was learned: A summary of the consensus discussion and conclusion will be presented.
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 | 0.120 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.012 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".