Importance of medication reconciliation in cancer patients
Bibliographic record
Abstract
Cancer patients are a complex and vulnerable population whose medication history is often extensive. Medication reconciliations in this population are especially essential, since medication discrepancies can lead to dire outcomes. This commentary aims to describe the significance of conducting medication reconciliations in this often-forgotten patient population. We discuss additional clinical interventions that can arise during this process as well. Medication reconciliations provide the opportunity to identify and prevent drug-drug and herb-drug interactions. They also provide an opportunity to appropriately adjust chemotherapy dosing according to renal and hepatic function. Finally, reconciling medications can also provide an opportunity to identify and deprescribe inappropriate medications. While clinical impact appears evident in this landscape, evidence of economic impact is lacking. As more cancer patients are prescribed a combination of oral chemotherapies, intravenous chemotherapies and non-anticancer medications, future studies should evaluate the advantages of conducting medication reconciliations in these patient populations across multiple care settings.
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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 | 0.013 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.025 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".