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 six common childhood cancers was established. A total of 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 73%-75%. 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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".