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Record W1952538459 · doi:10.1002/pon.3092

Development of an EORTC quality of life phase III module measuring cancer‐related fatigue (EORTC QLQ‐FA13)

2012· article· en· W1952538459 on OpenAlexaff
Joachim Weis, Juan Ignacio Arrarás, Thierry Conroy, Fabio Efficace, Claudia Fleissner, Attila Görög, Eva Hammerlid, Bernhard Holzner, Louise Jones, Anne Lanceley, Susanne Singer, Markus Wirtz, Hans‐Henning Flechtner, Andrew Bottomley

Bibliographic record

VenuePsycho-Oncology · 2012
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsCentre d'expertise et de recherche en infrastructures urbaines
Fundersnot available
KeywordsQuality of life (healthcare)IntrusivenessCancerDescriptive statisticsScale (ratio)PsychologyPsychosocialClinical trialCancer-related fatigueMedicinePhysical therapyClinical psychologyInternal medicinePsychiatryPsychotherapistStatistics

Abstract

fetched live from OpenAlex

BACKGROUND: European Organisation for Research and Treatment of Cancer (EORTC) has developed a new multidimensional instrument measuring cancer-related fatigue that can be used in conjunction with the quality of life core questionnaire, EORTC QLQ-C30. The paper focuses on the development of the phase III module, collaborating with seven European countries, including a patient sample of 318 patients. METHODS: The methodology followed the EORTC guidelines for developing phase III modules. Patients were assessed by questionnaires (EORTC QLQ-C30 with the EORTC Fatigue Module FA15) followed by an interview, asking for their opinions on the difficulty in understanding, on annoyance and on intrusiveness. RESULTS: The phase II FA15 was revised on the basis of qualitative analyses (comments of the patients), quantitative results (descriptive statistics) as well as the multi-item response theory analyses. The three dimensions (physical, emotional and cognitive) of the scale could be confirmed. CONCLUSIONS: As a result, EORTC QLQ-FA13 is now available as a valid phase III module measuring cancer-related fatigue in clinical trials and will be psychometrically improved in the upcoming phase IV.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.158
GPT teacher head0.442
Teacher spread0.284 · 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 teacher head, 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".

Quick stats

Citations75
Published2012
Admission routes1
Has abstractyes

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