The effect of palliative care consults on deprescribing in palliative cancer patients.
Bibliographic record
Abstract
92 Background: The transition from active cancer treatment to palliative care often results in a shift in drug risk-benefit assessment which requires the deprescribing of various medications. In addition, a change in patients’ goals of care (GOC) necessitates the alteration of drug therapy which includes both deprescribing and the addition of medications intended to improve quality of life. Depending on a patient’s GOC, a medication can be considered as inappropriate. Methods: The study was a one year retrospective database review and included cancer patients seen by the PCC team at the University of Alberta Hospital. Primary Objective: Comparison between potentially inappropriate medications (PIMs) prior to the palliative care consult (PCC) versus after the PCC. Secondary objective: Association between PIMs and GOC. The OncPal guidelines were used to identify and determine the number of PIMs prior to the PCC and after the PCC. Results: The reduction in PIMs prior to PCC versus after the PCC was 49% and was statistically significant (p < 0.001), demonstrating the PCC has a positive significant impact on deprescribing PIMs. For our secondary outcome, an overall decrease in PIMs was observed with the changes of GOC. This decrease in PIMs associated with GOC although not statistically significant, demonstrates that one of the benefits of a PCC is the GOC conversation. Conclusions: Deprescribing in palliative cancer patients can benefit patients by reducing their pill burden, decrease potentially side effects, and potentially decrease healthcare costs. This study shows the positive impact a PCC has on deprescribing and reassessing GOC. Furthermore, this study reveals the importance of using guidelines for deprescribing in palliative oncology and brings to light other medications that may be considered PIMs.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".