Integrating International Consensus Guidelines for Anticancer Drug Dosing in Kidney Dysfunction (ADDIKD) into everyday practice
Bibliographic record
Abstract
Part 2 of the International Consensus Guideline on Anticancer Drug Dosing in Kidney Dysfunction (ADDIKD) offers drug-specific consensus recommendations based on both evidence and practical experience. These recommendations build upon the kidney function assessment and classification guidelines established in Part 1 of ADDIKD. Here we illustrate how dosing recommendations differ between ADDIKD and existing guidance for four commonly used drugs: methotrexate, cisplatin, carboplatin and nivolumab. We then describe how the recommendations can be distilled into practice points for methotrexate and cisplatin. While ADDIKD is a significant improvement from previous guidelines, adoption of this new guideline requires further endorsement from key external stakeholders, 'change championing' by clinicians locally and encouraging its integration into existing reference sources, clinical trial protocols and electronic prescribing systems. Funding: Development of the ADDIKD guideline is funded by the NSW Government as part of the Cancer Institute NSW and received no funding from external commercial sources.
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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 | 0.047 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".