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Record W2891322203

[no title]

2018· article· fr· W2891322203 on OpenAlexaff
Catherine Dolye, Lauran Adams, Alison McAndrew, Stephanie Burlein‐Hall, Tracey DasGupta, Jennie Blake, Margaret I. Fitch

Bibliographic record

VenuePubMed · 2018
Typearticle
Languagefr
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsComputer science
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIF: Vérifier, par une évaluation psychométrique du questionnaire MENQOL (Menopause-Specific Quality of Life Questionnaire), un instrument d’autoévaluation ciblé, s’il peut servir à mesurer les symptômes de la ménopause chez les femmes atteintes d’un cancer gynécologique ou du sein. MÉTHODOLOGIE: Établir la validité apparente et de contenu du questionnaire MENQOL à l’aide d’experts, ainsi que sa fiabilité et sa validité conceptuelle auprès d’un groupe de femmes ayant reçu un diagnostic de cancer et souffrant d’une ménopause provoquée par le traitement. RÉSULTATS: En tout, 82 femmes ayant une ménopause provoquée par le traitement ont répondu aux questionnaires MENQOL, EORTC-C30 et SVQ. L’évaluation a permis de constater la validité apparente ainsi que la validité de contenu du questionnaire MENQOL, et sa fiabilité (homogénéité et test-retest) et sa validité (concourante et conceptuelle) se sont révélées acceptables. Par ailleurs, 85,5 % des femmes ont rapporté avoir ressenti des bouffées de chaleur. Les symptômes les plus incommodants ont toutefois été la prise de poids et la fatigue (sentiment d’épuisement). IMPLICATIONS: Le questionnaire MENQOL peut être utilisé pour évaluer les symptômes de ménopause provoquée par le traitement chez les femmes ayant reçu un diagnostic de cancer gynécologique ou du sein.

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)0.022
metaresearch head score (Gemma)0.082
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.852
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.082
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0050.006
Open science0.0030.004
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.1480.042

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 head0.030
GPT teacher head0.253
Teacher spread0.224 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2018
Admission routes1
Has abstractyes

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