Corrigendum: Cancer data quality and harmonization in Europe: The experience of the BENCHISTA Project – International Benchmarking of Childhood Cancer Survival by Stage
Bibliographic record
Abstract
Corrigendum: Cancer Data Quality and Harmonization in Europe: The experience of the BENCHISTA Project – International Benchmarking of Childhood Cancer Survival by Stage * Correspondence: angela.lopez@ucl.ac.uk; benchista@istitutotumori.mi.itKeywords: same as original articleCorrigendum on: Lopez-Cortes A, Didonè F, Botta L, Hjalgrim LL, Jakab Z, Canete Nieto A, Stiller C, Zeller B, Gatta G, Pritchard-Jones K and The BENCHISTA Project Working Group (2023) Cancer data quality and harmonization in Europe: the experience of the BENCHISTA Project – international benchmarking of childhood cancer survival by stage. Front. Oncol. 13:1232451. doi: 10.3389/fonc.2023.1232451 Incorrect Supplementary Material In the published article, there was an error in Supplementary Appendix (Number 3). The Project Working Group was displayed according to initial known information. A concern related to the Polish representative affiliation was raised after submission. After internal review and discussion with the relevant involved parties, an agreement on the amendment to the name/affiliation of the Polish representative and clarification to the source of data collection was reached. Furthermore, other changes to the Appendix 3 were incorporated as these were communicated by other representatives from the Project Working Group after the article deadline/submission. Supplementary appendix in published article and correct material statement displayed. Text CorrectionIn the published article, the first paragraph is correct. A phrase is required to be added in the first paragraph of the material and methods section to clarify that not all of the population-based cancer registries that contributed data to the project are constituted in the same way. A correction has been made to Materials and Methods, at end of first paragraph. This sentence previously stated:“All European population-based cancer registries (PBCRs) included in the EUROCARE studies were invited to participate in the BENCHISTA Project. Additionally other non-European PBCRs from Australia, Canada, Brazil, and Japan confirmed their contribution to the project. A great number of PBCRs are checked for quality indicators by the International Agency for Research on Cancer (IARC) based in four dimensions of quality: comparability, validity, timeliness, and completeness (16, 17).”The corrected sentence appears below:“All European population-based cancer registries (PBCRs) included in the EUROCARE studies were invited to participate in the BENCHISTA Project. Additionally, other non-European PBCRs from Australia, Canada, Brazil, and Japan confirmed their contribution to the project. A great number of PBCRs are checked for quality indicators by the International Agency for Research on Cancer (IARC) based on four dimensions of quality: comparability, validity, timeliness, and completeness (16, 17). Not all PBCRs have a government mandate. Some are coordinated by the National Society for Paediatric Haematology-Oncology and/or register all cases diagnosed at all hospitals authorised for childhood cancer treatment in the relevant country, with the aim of achieving population coverage.”The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
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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 | 0.021 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.087 | 0.047 |
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".