[The importance of correct policy options for oncologic patient care].
Bibliographic record
Abstract
During recent years, a growing number of papers have been published, showing how the treatment of malignant disease can meet, in various centres, with varying degrees of success. Doubtlessly, many more such papers would reach the journals hut for the lack of reliable follow-up data concerning survival, staging, type of treatment, selection criteria, etc. This article summarizes some data of 7 examples derived from observations in continental Europe, the UK, Canada and the US. Their most remarkable feature are the far from negligible differences in outcome (either in survival or in related "administrative" parametres such as treatment waiting time). Most of these studies stress the importance for oncologic patient care, of factors that are not directly related to primary clinical and/or scientific competence, but to policy and management aspects of the problem such as: providing adequate staff and technical infrastructure, creating cooperative networks and, last hut not least, organizing possibilities to collect, as widely as possible, enough data allowing a reliable appraisal of what has, or has not, been achieved.
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 | 0.031 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".