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Record W2094981332 · doi:10.1002/pbc.20096

Challenges to the measurement of health‐related quality of life in children receiving cancer therapy

2004· review· en· W2094981332 on OpenAlexaff
Paul C. Nathan, William Furlong, Ronald D. Barr

Bibliographic record

VenuePediatric Blood & Cancer · 2004
Typereview
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsHamilton Health SciencesMcMaster Children's HospitalMcMaster University Medical CentreMcMaster University
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Health related quality of lifeProxy (statistics)Pediatric oncologyChildhood cancerPediatric cancerPopulationCancer therapyCancerMEDLINEGerontologyFamily medicineEnvironmental healthDiseasePathologyNursingInternal medicine

Abstract

fetched live from OpenAlex

Measures of health-related quality of life (HRQL) assess those areas of a patient's functioning that are affected by their cancer and its therapy. Although HRQL measures are integrated frequently into studies of survivors of childhood cancer, their use in the assessment of children receiving therapy has been limited by several methodological challenges. These arise from issues specific to measuring HRQL in young children, who comprise a large proportion of the pediatric oncology population, and from issues associated with assessing HRQL during therapy, when the patient's health status is in constant flux. This study summarizes the commonly used HRQL measures, and examines factors that impact their broad application. These include the influence of developmental changes on the content and format of HRQL measures, the role of proxy assessors, the important characteristics of measurement tools used to assess patients receiving active therapy, and the issues related to the ideal timing of serial HRQL assessments in prospective trials.

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)0.009
metaresearch head score (Gemma)0.015
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: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0040.007
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.001

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 head0.162
GPT teacher head0.400
Teacher spread0.238 · 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
GenreReview

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

Citations44
Published2004
Admission routes1
Has abstractyes

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