ENHANCING INFORMATION ON STAGE AT DIAGNOSIS FOR CHILDHOOD CANCER IN AFRICA
Bibliographic record
Abstract
Background/Purpose Stage at diagnosis is an important metric in treatment and prognosis of cancer, and also in planning and evaluation of cancer control. In sub-Saharan Africa (SSA), for the latter, the only data source is the population-based cancer registry (PBCR). For childhood cancers, the “Toronto Staging Guidelines” have been developed to facilitate abstraction of stage by cancer registry personnel. Although the feasibility of staging using this system has been shown, there is limited information on the accuracy of staging. Methods A panel of case records of 6 common childhood cancers was established. 51 cancer registrars from 20 SSA countries staged these records, using Tier 1 of the Toronto guidelines. The stage that they assigned was compared with that decided by two expert clinicians. Results The registrars assigned the correct stage for 53-83% of cases (71% overall), with the lowest values for acute lymphocytic leukaemia (ALL), retinoblastoma and non Hodgkin lymphoma (NHL), and the highest for osteosarcoma (81%) and Wilms tumour (83%). For ALL and NHL, many unstageable cases were mis-staged, probably due to confusion over the rules for dealing with missing data; for the cases with adequate information, accuracy was 72-73%. Some confusion was observed over the precise definition of three stage levels of retinoblastomas. Conclusions A single training in staging resulted in an accuracy, for solid tumours, that was not much inferior to what has been observed in high income settings. Nevertheless, some lessons were learned on how to improve both the guidelines, and the training course.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".