Pharmacist-led monitoring program for patients on sunitinib for metastatic renal-cell carcinoma: A Canadian experience.
Bibliographic record
Abstract
479 Background: Sunitinib is a standard of care for first-line treatment of metastatic clear cell renal-cell carcinoma (mRCC). Despite having a relatively good safety profile, Sunitinib does have several clinically important toxicities. With the rapid rise in the use of Sunitinib and other oral cancer agents, we instituted a pharmacist-led monitoring program in the ambulatory care setting to prospectively document, monitor, and manage toxicities in our Canadian province. Methods: The pharmacist-led monitoring program consisted of patient assessments in clinic with the oncology team combined with a call back program. The program consisted of a patient assessment in the oncology clinic on day 1 of a Sunitinib cycle followed with a call back on day 14. A chart review of consecutive patients who were prospectively monitored by this progam after receiving Sunitinib for mRCC was conducted. Treatment specific data for the first six cycles of therapy included dose reductions, therapy delays/interruptions, therapy discontinuation, and reason for each was recorded. Toxicity data including the occurrence and severity grade was collected. The time to treatment failure (TTF) defined as the time from therapy initiation to treatment discontinuation for any reason was measured. Results: Fifty six patients are included in the study cohort. Of these, 52 (92.86%) started at the standard 50 mg once daily and the remainder at a reduced dose. Additionally, 15 patients experienced hypertension requiring drug therapy adjustment or additional antihypertensive therapy. Two patients required drug therapy for hypothyroidism. There were a total of 39 dose reductions in this patient population over a six cycle period. The majority of dose reductions (46.2%) and therapy interruptions (34.5%) occurred during cycle one. The time to treatment failure for the 45 patients that discontinued therapy was 9.72 months. Conclusions: Pharmacist-Led monitoring of oral cancer therapies is a practical and feasible method of monitoring patients on Sunitinib for mRCC. The success has led to its implementation with other agents and disease sites.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".