Design and Implementation of the Retinoblastoma Collaborative Laboratory
Bibliographic record
Abstract
PURPOSE: The purpose of this work was to describe the design and implementation of a digital pathology laboratory, the Retinoblastoma Collaborative Laboratory (RbCoLab) in Kenya. METHOD: The RbCoLab is a central lab in Nairobi that receives retinoblastoma specimens from all over Kenya. Specimens were processed using evidence-based standard operating procedures. Images were produced by a digital scanner, and pathology reports were disseminated online. RESULTS: The lab implemented standard operating procedures aimed at improving the accuracy, completeness, and timeliness of pathology reports, enhancing the care of Kenyan retinoblastoma patients. Integration of digital technology to support pathology services supported knowledge transfer and skills transfer. A bidirectional educational network of local pathologists and other clinicians in the circle of care of the patients emerged and served to emphasize the clinical importance of cancer pathology at multiple levels of care. A 'Robin Hood' business model of health care service delivery was developed to support sustainability and scale-up of cancer pathology services. DISCUSSION: The application of evidence-based protocols, comprehensive training, and collaboration were essential to bring improvements to the care of retinoblastoma patients in Kenya. When embraced as an integrated component of retinoblastoma care, digital pathology offers the opportunity for frequent connection and consultation for development of expertise over time.
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".