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Record W3121642377 · doi:10.2196/21851

Health-Related Quality of Life in European Childhood Cancer Survivors: Protocol for a Study Within PanCareLIFE

2021· article· en· W3121642377 on OpenAlexvenueno aff
Gabriele Calaminus, Katja Baust, Claire Berger, Julianne Byrne, Harald Binder, Léonie Casagranda, Desiree Grabow, Martha A. Grootenhuis, Peter Kaatsch, Melanie Kaiser, Tomáš Kepák, Kateřina Kepáková, Jarmila Kruseová, Aleš Lukš, Claudia Spix, M. van den Berg, Marry M. van den Heuvel‐Eibrink, Eline van Dulmen‐den Broeder, Rahel Kuonen, Grit Sommer, Claudia E. Kuehni

Bibliographic record

VenueJMIR Research Protocols · 2021
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuality of life (healthcare)CancerCancer registryCohortGerontologyPathology

Abstract

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BACKGROUND: Survival after childhood cancer has improved to more than 80% during the last few years, leading to an increased number of childhood cancer survivors. Cancer itself, or its treatment, may cause chronic health conditions, including somatic and mental sequelae, which may affect survivors' health-related quality of life (HRQoL). OBJECTIVE: The project PanCareLIFE aims to establish a large database with comprehensive data on childhood cancer survivors from different European countries, including data on HRQoL. Within PanCareLIFE, this study aims to describe HRQoL in survivors, investigate predictors of HRQoL, and describe the association of HRQoL with hearing and female fertility impairment. This paper describes the design of the HRQoL study, the origin of data, strategies for data collection, and sampling characteristics of survivors from each contributing country. METHODS: A total of 6 institutions from 5 European countries (the Czech Republic, France, Germany, the Netherlands, and Switzerland) provided data on HRQoL assessed with the Short Form 36 and on relevant predictors. The central PanCareLIFE data center aggregated the data and harmonized the variables between the institutions. Survivors were eligible if they received a diagnosis of cancer according to the 12 main groups of the International Classification of Childhood Cancer, 3rd edition, or Langerhans cell histiocytosis; were aged ≤18 years at the time of diagnosis; were residents of the respective country at the time of diagnosis; had survived ≥5 years after cancer diagnosis; were aged ≥18 years at the time of the questionnaire survey; and did not refuse to registration in the national or local childhood cancer cohort. RESULTS: We identified 24,993 eligible survivors. Of those, 19,268 survivors received a questionnaire and 9871 survivors participated, resulting in response rates of 9871/24,993 (39.50%) of eligible survivors and of 9871/19,268 (51.23%) invited survivors. Most participants were diagnosed with cancer between the ages of 10 and 14 years (3448/9871, 34.93%) or <5 years (3201/9871, 32.43%). The median age was 8 years. Of the 9871 participants, 3157 (31.97%) were survivors of leukemia, 2075 (21.02%) lymphoma, and 1356 (13.7%) central nervous system (CNS) tumors. Most participants (9225/9871, 93.46%) had no history of a subsequent tumor; 77.45% (7645/9871) received chemotherapy with or without other treatments. More than half (5460/9871, 55.31%) were aged 25 to 34 years at the time of the HRQoL study. Participating survivors differed from nonparticipants; participants were more often women, survivors of leukemia or lymphoma, and less frequently, survivors of CNS tumors than nonparticipants. CONCLUSIONS: PanCareLIFE successfully assessed HRQoL and its predictors in 9871 European survivors of childhood cancer. This large population will permit detailed investigations of HRQoL after childhood cancer, particularly the impact of hearing and female fertility impairment on HRQoL. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/21851.

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)0.025
metaresearch head score (Gemma)0.014
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: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.026
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.014
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0260.005

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 head0.375
GPT teacher head0.592
Teacher spread0.217 · 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
GenreProtocol

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".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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