Patient Engagement in the Management of Pituitary Tumor: University of Ottawa Experience
Bibliographic record
Abstract
Background: Pituitary adenomas are common and often require complex multidisciplinary care with multiple specialists. This may result in a health care system that is challenging for patients to navigate. Audits of care at our institution revealed opportunities for improvement to better align care with patients' needs. Methods: A quality improvement initiative that incorporated a patient advisory committee of patients who had received treatment for pituitary adenoma at our center and their family members was used to help identify opportunities for improvement. The patient-identified gaps in care included the need to coordinate and minimize appointments and the desire for better communication and education. Based on this information, changes were implemented to the pituitary program, including increasing access to the multidisciplinary clinic and developing a standardized and centralized triage process. Results: A pre and postintervention analysis consisting of retrospective chart reviews revealed that these changes had an impact on wait times for first assessment, and a significant shift in location of this first visit, with a larger proportion of patients being seen in the multidisciplinary clinic after intervention. Conclusion: We demonstrate that patient involvement, beyond individual patient–physician interactions, can lead to meaningful and observable changes and can improve the quality of care for pituitary adenoma. Publication History Article published online: 12 February 2021 © 2021. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".