Clinical Pathway and Patient Navigation: Research Protocol on the Appropriateness, Timeliness and Support of Women Diagnosed with Breast Cancer in Abia Stat
Bibliographic record
Abstract
Background: Breast cancer is the second most common malignancy affecting Nigerian women, and contributes the highest cancer-related mortality in this population. Despite the rising prevalence of breast cancer, Nigerian healthcare professionals do not have adequate resources in screening, diagnosing, treating and follow up of women with breast cancer. The objective of this study was to understand how the development and implementation of a state-wide clinical pathway alongside a patient navigation program will impact the care providers and care receiver (beast cancer patients). Methods: This mixed methods, cross-sectional study will develop and deploy a multidisciplinary clinical pathway focused on breast cancer management. Trained patient navigators will facilitate the implementation of the pathway and to support patients. An electronic medical record system will be deployed to document the use of the pathway. Mixed methods data will be collected periodically, including patient satisfaction, treatment adherence, psychosocial outcomes, and quality of life. Qualitative data will provide contextual details.Anticipated Result and Discussion: This research will potentially structure the management of breast cancer in a way that optimizes available resources while reducing delays in Abia state, Nigeria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".