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Record W1532047090 · doi:10.1111/ecc.12173

Economic evaluation of treatment for acute lymphoblastic leukaemia in childhood

2014· article· en· W1532047090 on OpenAlexafffundabout
Charlene Rae, William Furlong, Momcilo Jankovic, Albert Moghrabi, Ahmed Naqvi, Alessandra Sala, Yves Samson, Sonja Depauw, David Feeny, Ronald D. Barr

Bibliographic record

VenueEuropean Journal of Cancer Care · 2014
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity of AlbertaHospital for Sick ChildrenCentre hospitalier de l'Université LavalCentre Hospitalier Universitaire Sainte-JustineMcMaster University
FundersNational Cancer InstituteHospital for Sick ChildrenMcMaster University
KeywordsMedicinePediatricsQuality of life (healthcare)Quality-adjusted life yearCost effectiveness

Abstract

fetched live from OpenAlex

Berlin-Frankfurt-Munster (BFM) and Dana-Farber Cancer Institute (DFCI) consortia's treatment strategies for acute lymphoblastic leukaemia (ALL) in children are widely used. We compared the health effects and monetary costs of hospital treatments for these two strategies. Parents of children treated at seven centres in Canada, Italy and the USA completed health-related quality of life (HRQL) assessments during four active treatment phases and at 2 years after treatment. Mean HRQL scores were used to calculate quality-adjusted life years (QALYs) for a period of 5 years following diagnosis. Total costs of treatment were determined from variables in administrative databases in a universally accessible and publicly funded healthcare system. Valid HRQL assessments (n = 1200) were collected for 307 BFM and 317 DFCI patients, with costs measured for 66 BFM and 28 DFCI patients. QALYs per patient were <1.0% greater for BFM than DFCI. Median HRQL scores revealed no difference in QALYs. The difference in mean total costs for BFM (US$88 480) and DFCI (US$93 026) was not significant (P = 0.600). This study provides no evidence of superiority for one treatment strategy over the other. Current BFM or DFCI strategies should represent conventional management for the next economic evaluation of treatments for ALL in childhood.

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 score0.813
Threshold uncertainty score0.558

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)0.0000.000

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 head0.032
GPT teacher head0.348
Teacher spread0.316 · 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

Citations20
Published2014
Admission routes3
Has abstractyes

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