Inventory of Cancer Guidelines: a tool to advance the guideline enterprise and improve the uptake of evidence
Bibliographic record
Abstract
The Inventory of Cancer Guidelines (ICG) was designed to mitigate challenges associated with inconsistencies in the quality of cancer guidelines, keeping guidelines current and the duplication of effort in guideline development. The ICG is a searchable database of quality-appraised guidelines in cancer control that also includes designations of guidelines in progress, those in need of an update and those currently being updated. From a clinical perspective, the majority of the completed guidelines target breast, lung, colorectal and prostate cancers, and focus on the treatment stage of the cancer continuum. There is considerable variability in guideline quality both within and across guideline developers, as measured by the Appraisal of Guidelines for Research and Evaluation II. Quality domains of applicability and editorial independence are the guideline quality domains that score the poorest. While the ability to inform on the status of cancer control guidelines is important, the real potential of the ICG is in its ability to leverage positive change in the guideline enterprise. Pilot projects are underway to use data from the ICG to tailor audit and feedback interventions for guideline developers and to pursue collaborative updating and guideline adaptation initiatives, using the ICG as the platform from which these partnerships can evolve.
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 | 0.221 | 0.384 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.043 | 0.038 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".