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Progress by international collaboration for pediatric renal tumors by HARMONIsation and Collaboration: the HARMONICA initiative

2022· preprint· en· W4304187220 on OpenAlexaff
James I. Geller, Marry van den Heuvel‐Eibrink, Conrad V. Fernandez, Norbert Graf

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRenal and related cancers
Canadian institutionsIzaak Walton Killam Health Centre
Fundersnot available
KeywordsMedicineCogChemotherapyRadiation therapyWilms' tumorClinical trialPathologicalInternal medicineOncologyCancer

Abstract

fetched live from OpenAlex

IntroductionSince the initial attempts to treat children with renal cancer over 50 years ago, outcome for children with renal cancer has generally become promising. While the first endeavors mainly included surgical treatment, in the early 60s radiotherapy and chemotherapy were introduced, leading to cure of patients, including some with metastatic disease. (1) Since then, overall survival rates for the most common type of renal tumors in childhood (nephroblastoma or Wilms tumor) have improved to more than 90 percent. These excellent treatment outcomes are similar in the 2 largest clinical trial groups (the Children’s Oncology Group Renal Tumor Committee (COG-RTC; former National Wilms Tumor Study Group (NTWSG)), and the International Society of Pediatric Oncology Renal Tumor Study Group (SIOP-RTSG). Despite the difference in upfront treatment choice (primary surgery when feasible (COG-RTC) or preoperative chemotherapy (SIOP-RTSG)) both groups have optimized the stratification of patients in their trials by modifying the intensity of treatment according to individual risk factors, in order to improve outcome for high-risk renal tumor types, but also to reduce early and late toxicity in lower and intermediate risk tumors as much as possible. (2-4)This improvement in risk stratification has resulted in better outcomes and less cancer related toxicity. However, for remaining small subgroups of pediatric renal tumor patients, with very poor outcomes, further understanding of the underlying biology, in correlation with clinic-pathological characteristics, is an unmet need. Further, standard multidisciplinary treatment (surgery, radiotherapy, chemotherapy) can be challenging to access and/or deliver in some low and middle income countries (LMIC). The power inherent in international collaboration to address these challenges was a driving principle that supported the creation of the HARMONICA (HARMONIzation and COllaboration) initiative in 2015, when we established an organized collaborative structure for transatlantic experts from COG-RTC and SIOP-RTSG. The mandate of HARMONICA is to identify specific challenges for pediatric renal tumor subsets in order to meet the aims of our global approach to cure every child with a renal tumor with limited toxicity.The HARMONICA group meets at least once a month by videoconferences, and as much as possible also face to face, at least once or twice a year, during existing pediatric cancer conferences. In addition, several transatlantic HARMONICA expert subgroups are collaborating on specific topics. All work is currently done by a tremendous engagement of many enthusiastic members of both study groups. Despite the fact of obvious advantages, HARMONICA is still lacking funding and needs to optimize their structure as a legal entity. Notwithstanding such limitations, in this special issue of PBC, we present the achievements, the challenges, and the future perspectives, identified by these expert groups.

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)0.082
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.082
Threshold uncertainty score0.434

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0820.043
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.005
Science and technology studies0.0020.003
Scholarly communication0.0100.004
Open science0.0070.016
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0210.010

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 head0.010
GPT teacher head0.277
Teacher spread0.267 · 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 designNot applicable
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
Published2022
Admission routes1
Has abstractyes

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