Deprescribing: A Prime Opportunity to Optimize Care of Cancer Patients
Bibliographic record
Abstract
Patients with incurable cancers have an increasing number of comorbidities, which can lead to polypharmacy and its associated adverse events (drug-to-drug interaction, prescription of a potentially inappropriate medication, adverse drug event). Deprescribing is a patient-centered process aimed at optimizing patient outcomes by discontinuing medication(s) deemed no longer necessary or potentially inappropriate. Improved patient quality of life, risk reduction of side effects or worse clinical outcomes, and a decrease in healthcare costs are well-documented benefits of deprescribing. Deprescribing and advance care planning both require consideration of patients' values, preferences, and care goals. Here, we provide an overview of comorbidities and associated polypharmacy risks in cancer patients, as well as useful tools and resources for deprescribing in daily practice, and we shed light on how deprescribing can facilitate advance care planning discussions with patients who have advanced cancer or a limited life expectancy.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".