External quality assurance of breast cancer pathologic reporting in Kenya
Bibliographic record
Abstract
e11586 Background: Eighty percent of women diagnosed with breast cancer (BC) in East Africa present with advanced disease; current literature suggests a preponderance of triple negative/basal like breast cancer in this subset of African women. These studies are limited by their retrospective nature, small numbers, and unclear quality of pathologic specimen reporting. The objective of this study is to provide external quality assurance (EQA), quality control, and validation of hormone receptor and Her2 status of breast cancer specimens from Kenya. Methods: 108 retrospectively identified BC tumour blocks from the Aga Khan University Hospital (Nairobi, Kenya) during 2006–2008 will undergo repeat pathologic assessment for estrogen receptor (ER), progesterone receptor (PR), and Her2 status at Sunnybrook Health Sciences Center (Toronto, Canada).Currently at the Aga Khan University Pathology Lab, ER,PR and Her2 testing is performed manually once every two weeks using Heat Induced Antigen Retrieval and Dako reagents including the ENVISION detection system. Parallel controls of known tissue reactivity are also run; however there is currently no formal EQA. Results: Results will be used to identify areas of improvement in specimen handling and pathology reporting. Conclusions: Standardized and accurate pathologic assessment of BC specimens in East Africa is essential for establishing centres of excellence in Kenya and the wider East African region for hormone receptor and Her2 neu analysis. Results would contribute to understanding the prevalence of triple negative disease in East Africa, lead to improved treatment recommendations and patient outcomes, and serve as a foundation for prospective studies in East Africa. No significant financial relationships to disclose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.083 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".