Utilizaton of patient reported outcomes measures (PROM) to characterize symptom burden and adherence associated with oral oncolytic therapy.
Bibliographic record
Abstract
210 Background: The use of patient reported outcome measures (PROMs) to monitor cancer treatment tolerability has been shown to positively impact outcomes. The purpose of this study was to characterize the incidence and severity of side effects, patient self-management confidence, and medication adherence in patients receiving oral oncolytic therapy. Methods: This multicenter, cross-sectional, observational study was conducted across 6 Michigan oncology practices from July 2016-December 2018. Patients were eligible to complete PROMs during the course of their treatment if they were receiving an oral oncolytic medication (excluding endocrine therapy). Results: There were a total of 2252 PROMs completed in 695 patients. Patients were 48% female, a median age of 69 years, and most commonly receiving treatment with capecitabine (18%), palbociclib (10%), and lenalidomide (9%). 54% of PROMs had at least one Edmonton Symptom Assessment Scale (ESAS) symptom rated as moderate or severe. Patients indicated the presence of a most bothersome symptom (MBS) in 35% of PROMs. Most common MBSs were fatigue (26%), pain (16%), constitutional symptoms other than fatigue (15%), and nausea/vomiting (14%). Non-adherence was reported in 20% of PROMs. ESAS symptoms rated as moderate or severe, the presence of a MBS, and lower confidence scores all correlated with medication non-adherence. Conclusions: Patients taking oral oncolytics for their cancer treatment experience a high symptom burden with more than 50% experiencing a moderate to severe symptom. Optimizing symptom management and providing patient education that increases patient confidence in self-management may improve medication adherence and patient outcomes.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".