Resource stratified guidelines for cancer: Are they all the same? <scp>Interguideline</scp> concordance for systemic treatment recommendations
Bibliographic record
Abstract
Abstract A number of organizations are producing resource stratified guidelines (RSGs) for cancer. Despite using similar definitions of resource levels, systemic treatment recommendations often differ between organizations. We systematically searched for RSGs focusing on solid tumors. We qualitatively compared the methods used to generate guidelines using the AGREE‐II appraisal tool. We extracted systemic treatment recommendations and assessed interguideline concordance using the Gwet AC1 coefficient, stratified by resource level, treatment setting and cancer type. We identified 69 RSGs cancer covering 15 solid tumors produced by four organizations. Despite using common resource‐level definitions (Basic, Core/Limited, Enhanced and Maximal), recommendations differed between organizations. Concordance for chemotherapy recommendations was poor in Basic (58.3%, Gwet 0.20), fair in Core (58.3%, Gwet 0.32) and excellent in Enhanced (92.4%, Gwet 0.92) and Maximal settings (95.4%, Gwet 0.95). Concordance rates for endocrine therapy were good in Basic (80% Gwet 0.61), and excellent in Core (90%, Gwet 0.87), Enhanced (90%, Gwet 0.89) and Maximal settings (90%, Gwet 0.89). There was moderate to excellent concordance in targeted therapy recommendations across all resource levels. Differences in recommendations appeared driven by different opinions among the chosen panel of experts regarding what is resource appropriate. Overall, we found that countries looking to base treatment and health‐policy on RSGs will find conflicting information depending on which guidelines are used, particularly for chemotherapy in Basic and Core settings. Improved transparency regarding the methods used to determine the value of a therapy for a given resource level is needed.
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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 | 0.191 | 0.568 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".