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Record W1858551680 · doi:10.1177/0003489415612801

Improving Quality of Life With Nabilone During Radiotherapy Treatments for Head and Neck Cancers

2015· article· en· W1858551680 on OpenAlexafffund
Mathieu Côté, Mathieu Trudel, Changshu Wang, A. Fortin

Bibliographic record

VenueAnnals of Otology Rhinology & Laryngology · 2015
Typearticle
Languageen
FieldMedicine
TopicOral health in cancer treatment
Canadian institutionsUniversité LavalCentre Hospitalier Universitaire de SherbrookeCentre hospitalier universitaire de Québec
FundersCanadian Institutes of Health Research
KeywordsNauseaPlaceboMedicineQuality of life (healthcare)Radiation therapyHead and neck cancerMoodAnesthesiaSurgeryPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: Patients treated for head and neck carcinomas experience a significant deterioration of their quality of life during treatments because of severe side effects. Nabilone has many properties that could alleviate symptoms caused by radiotherapy and improve patients' quality of life. The aim of the present study was to compare the effects of nabilone versus placebo on the quality of life and side effects during radiotherapy for head and neck carcinomas. METHODS: Fifty-six patients were randomized to nabilone or placebo. Patients filled the European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 and the EORTC QLQ-H&N35; three independent questionnaires assessing appetite, nausea, and toxicity; and a visual analog scale for pain. These data were collected before radiotherapy, each week during radiotherapy, and 4 weeks after radiotherapy. Patients were weighed every week. RESULTS: Nabilone did not lengthen the time necessary for a 15% deterioration of quality of life (P = .4279), and it was not better than placebo for relieving symptoms like pain (P = .6048), nausea (P = .7105), loss of appetite (P = .3295), weight (P = .1454), mood (P = .3214), and sleep (P = .4438). CONCLUSION: At the dosage used, nabilone was not potent enough to improve the patients' quality of life over placebo.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.140
GPT teacher head0.420
Teacher spread0.280 · 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

Citations72
Published2015
Admission routes2
Has abstractyes

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