Utilizing patient reported outcomes for patients recieving oral chemotherapy.
Bibliographic record
Abstract
190 Background: Management of oral chemotherapy presents many challenges to oncology practitioners. The purpose of this study is to describe how incorporation of patient reported outcomes (PRO) for patients receiving oral chemotherapy can identify those patients who are experiencing moderate to severe symptom burden and nonadherence. Methods: As part of a statewide quality collaborative, we wished to improve our monitoring of patients receiving oral chemotherapy. The quality collaborative created a PRO assessment that includes a revised Edmonton Symptom Assessment Scale (ESAS), a single-item adherence question, reasons for nonadherence, the patient’s most bothersome symptom and questions related to patient confidence. Our medical assistants provide the assessment to the patient before each appointment. Results: Patients completing the PRO during the first 3 months (7/7/16 – 9/27/16) were evaluated. We had 32 assessments completed by 23 patients. The oral chemotherapy prescribed were capecitabine (48%), erlotinib (13%), temozolomide (13%), and not recorded (26%). Of the 29 completed ESAS assessments, 72% included at least 1 moderate side effect, and 48% included at least 1 severe side effect. 29% of patients reported low-moderate confidence to self-manage their symptoms. Less than excellent adherence (<80% adherence) was reported in 30% of patients with the most commonly reported reason being related to side effects or concerns about side effects. Conclusions: Use of PROs in our oral chemotherapy population identified a large proportion of patients experiencing moderate to severe side effects. Further assessment of how this compares to what patients report to their oncologist during their visits will be reviewed. In addition, we found that approximately 30% of our patients are nonadherent to their oral chemotherapy. This is consistent with recent publications. We plan to continue assessing patient outcomes and utilizing the data we collect to improve patient self-management support.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".