Pathology Cancer Clinic—An Innovative Model to Enhance the Quality of Patient Care for Women With Gynecologic Cancer at The Ottawa Hospital
Bibliographic record
Abstract
Gynecologic cancer is a major issue in women’s health, with ovarian cancer representing the fifth leading cause of cancer-related death. Coping with a cancer diagnosis can be overwhelming for a multitude of reasons, including the breadth of information received. Pathology reports are a valuable resource; however, they can be difficult to understand due to the specialized language utilized. The objective was to assess if patients’ involvement in a pathology clinic-based setting will improve their understanding of their diagnosis and contribute to enhanced satisfaction in their quality of care. Interested patients are recruited from the gynecology oncology clinics. During the clinic appointment, which is led by anatomical pathology residents, pathology reports and histological slides are reviewed in detail. Patients are asked to complete survey questionnaires before and after the session. Survey data are collated to determine if the consultation experience was beneficial. We are currently in the process of recruitment, having interviewed five patients, and our preliminary results demonstrate the clinic concept has a positive impact for patients. Our goal is to recruit at least 20 patients, which will allow us to draw meaningful conclusions on the impact of a pathology clinic-based setting. This project is based on the collaboration of a multidisciplinary health care team to reinforce a culture of patient-focused care. We expect that patients will find the experience to be a positive, which will contribute to their involvement in their management plans. Ultimately, we hope this research will lead to the successful implementation of pathology clinics in both residency training programs and tertiary care centers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".