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Record W2765623626 · doi:10.5737/23688076274356364

Centres de diagnostic rapide du cancer et conséquences psychologiques : une analyse systématique

2017· article· fr· W2765623626 on OpenAlexaffvenue
Mina Singh, Christine Maheu, Teresa J. Brady, Rachel Farah

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2017
Typearticle
Languagefr
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPolitical scienceGynecologyMedicinePhilosophy

Abstract

fetched live from OpenAlex

L’objectif de la présente analyse est d’évaluer l’état des travaux étudiant les effets psychologiques des centres de diagnostic rapide (CDR) sur les femmes en processus de dépistage d’un cancer du sein et d’en identifier les répercussions sur la pratique infirmière et la recherche à venir. Pour ce faire, une recension systématique des écrits a été réalisée, puis les données de six études ont été extraites et analysées. Selon les données probantes, les CDR réduiraient l’anxiété à court terme chez les femmes qui doivent subir des tests de dépistage supplémentaires et dont la tumeur se révèle bénigne. Il y a, somme toute, peu de données disponibles concernant les conséquences de l’anxiété sur les femmes qui reçoivent un diagnostic de cancer dans un CDR, mais certains éléments semblent indiquer que ce sous-groupe serait davantage touché par la dépression à long terme. Les infirmières doivent être sensibles aux différents besoins des femmes qui obtiennent le jour même les résultats des tests supplémentaires qu’elles doivent subir après un dépistage de cancer.

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)0.046
metaresearch head score (Gemma)0.140
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.079
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.140
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0060.012
Science and technology studies0.0020.001
Scholarly communication0.0070.004
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.029
GPT teacher head0.388
Teacher spread0.359 · 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 designSystematic review
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

Citations0
Published2017
Admission routes2
Has abstractyes

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