A feasibility study to assess the integration of a pharmacist into neurooncology clinic
Bibliographic record
Abstract
OBJECTIVE: A multidisciplinary approach is increasingly used in NeuroOncology clinics. Although this model has several advantages, patients report feeling overwhelmed by the complexity of their treatment protocol and staff feel rushed because each provider must evaluate the patient within the limited clinic hours. We hypothesized that the presence of a pharmacist in clinic could address these concerns by (1) reviewing all treatment protocols and side-effect management with patients, (2) being available to address questions outside of clinic and (3) answering staff related medication questions. METHODS: The pharmacist met with consenting patients at the initial clinic visit and followed up by telephone two additional times. The pharmacist was available to answer questions outside of clinic hours. Surveys were developed and given to patient and staff to evaluate their experience. RESULTS: Over 4 months, 13 patients were enrolled. The pharmacist interacted with each patient an average of 9 times with 55% of interactions occurring outside scheduled visits and two-thirds of pharmacist interventions directly involving patient care. A total of 85% of patients and staff responded to the evaluation survey and 90% of respondents indicated that the pharmacist should remain part of the NeuroOncology team. Patients reported less stress related to their treatment and clinical staff experienced improved clinical efficiency directly as a result of the presence of the pharmacist. CONCLUSION: Based on these results, a clinical pharmacist should become a permanent member of the outpatient NeuroOncology clinic.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".