An interprofessional pathway for patients initiating treatment with palbociclib: Optimization of toxicity monitoring.
Bibliographic record
Abstract
268 Background: CDK 4/6 Inhibitors such as palbociclib in combination with hormonal therapy are considered the new standard of care for eligible advanced breast cancer patients with ER positive, HER2 negative disease. These agents have toxicities warranting standardized monitoring algorithms. Methods: An interprofessional pathway was created for palbociclib that identified key patient milestones and responsibilities of different care providers. Blood work (BW) parameters for dose interruption/reduction were specified, with a minimum absolute neutrophil count (ANC) of 1 as the threshold for continuing therapy. A retrospective chart audit was conducted to assess adherence to the biweekly BW and toxicity assessment pathway. Toxicity results were compared to the PALOMA-2 trial. Results: Consecutive patients were assessed between June 2016 and August 2017. Median follow up was 193 days. Twenty five patients before and 24 patients post algorithm implementation were included. Median age was 59, and 80% had >2 lines of prior systemic treatment. Dose reductions were observed in 57% of patients, 84% for neutropenia (Table 1). This is higher than documented in PALOMA-2. Instances where BW and clinic assessment were indicated and completed went from 43% to 69% and 32% to 67% respectively. Conclusions: Our study included heavily pre-treated patients and used an ANC treatment continuation threshold of 1 as opposed to 0.5; this might explain the increased incidence of neutropenia and dose modifications. This algorithm, with defined provider roles and patient milestones, appears feasible and effective in standardizing follow up and optimizing patient care.[Table: see text]
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".