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Record W4389704675 · doi:10.1017/s0266462323000673

OP22 Benchmarking Of Population-Based Childhood Cancer Survival By Toronto Stage: Know The Differences To Propose Effective Interventions

2023· article· en· W4389704675 on OpenAlexaboutno aff
Rosalia Ragusa, Dott Fabio Didonè, Laura Botta, Antonina Torrisi, Maria Bellia, Gemma Gatta

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2023
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePopulationStage (stratigraphy)BenchmarkingCancerMedical diagnosisDocumentationPediatric cancerPsychological interventionEpidemiologySurvival rateInternal medicinePathologyEnvironmental health

Abstract

fetched live from OpenAlex

Introduction Pediatric cancers are rare tumors, heterogeneous in location and biologically very different from adult cancers. Documented survival variation across European countries and Italian regions shows that there is still room for further improvement by reducing inequalities. We aim to understand why there are differences in survival. The BENCHISTA-ITA project (National Benchmarking of Childhood Cancer Survival by Stage at diagnosis), that is the Italian twin project of the International BENCHISTA, collects stage at diagnosis of solid pediatric tumors, according to the Toronto Guidelines. We will compare how far the cancer has spread at diagnosis and test if differences in tumor stage explain any survival differences between Italian regions. Methods The project study involved the stage distribution and the survival of 9 pediatric solid tumors diagnosed between 2013 and 2017 in Italy. All patients therefore had at least 3 years of follow-up in 2021 for life-stage definition. The study involves the identification of all new diagnoses of cancer, evaluation of the clinical documentation of cases eligible for research, and international classification and coding. Analyses of stage distribution and survival rates for each tumor type will be described. Results Data from 35 population-based cancer registries from 18 out of 20 Italian regions were collected covering about 84 percent of the Italian child population. In particular, data on: imaging/examination performed before any treatment; source used for staging; primary treatment defined as given within one year from diagnosis; relapse/ recurrence/ progression; follow up and status of life. The study tested the applicability of the Toronto Guidelines as a tool to obtain population-level comparable stage information for childhood cancers. There were 1,343 cases collected (242 Neuroblastoma, 124 Wilms Tumour, 145 Medulloblastoma, 148 Osteosarcoma, 135 Ewing sarcoma, 115 Rhabdomyososarcoma, 54 Ependymoma, 47 Retinoblastoma, 333 Astrocytoma). Toronto stage could be assigned in more than 90 percent in the majority of tumors. Tumors in which it was more difficult to assign the stage using the Toronto staging guidelines were ependymoma, astrocytoma, and retinoblastoma. It was easier to retrieve data for patients in the 0-14 years of age range than adolescents (14-18 years). Differences in stage distribution and survival differences between regional grouping were presented. Conclusions The Italian BENCHISTA project, improving the connection between pediatric cancer registries, aims to improve care of children with cancer across the nation, reducing possible disparities. The wide adoption of the Toronto Guidelines will facilitate international comparative incidence studies, strengthen the interpretation of survival data, and contribute to more appropriate solutions to improve childhood cancer outcomes.

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 imitation

Not 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.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.071
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.334

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.071
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.028
GPT teacher head0.443
Teacher spread0.415 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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