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Record W3216364107 · doi:10.32920/ihtp.v1i3.1464

Head and neck cancer treatment and its sexual impact on quality of life: An integrative literature review.

2021· article· en· W3216364107 on OpenAlexaffvenue
Ricardo Souza Evangelista Sant’Ana, Ana Dulce Dos Santos, Felipe Santos da Silva, Rodrigo Almeida Bastos, Carmen Sílvia Passos Lima, Christine Maheu, Egberto Ribeiro Turato, Simone de Godoy

Bibliographic record

VenueInternational Health Trends and Perspectives · 2021
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsCINAHLDisfigurementHuman sexualityQuality of life (healthcare)MedicineHead and neck cancerAffect (linguistics)Ethnic groupPsychologyCancerNursingSurgeryGender studies

Abstract

fetched live from OpenAlex

Objective: To identify the impacts of head and neck cancer treatment on the sexual health of patients. Methods: An integrative literature review was carried out from May to October 2020 using PubMed, CINAHL, Web of Science, LILACS, and the SciELO portal. A total of 287 primary articles were identified. After assessing them, 6 articles met the eligibility criteria, which were: all articles published in the last ten years that addressed the sexual impact of HNC treatment on people's lives, without any language or age. Results: Patients with Head and Neck Cancer have to face aesthetic disfigurement challenges in post-treatment. This leads to a greater degree of suffering and social and sexual problems than is observed in other cancer patients. Health professionals do not feel safe to access the intimate and sexual demands of patients during the clinical treatment. Conclusions: Most of the studies included in this review focused on measuring the quality of life using only one or two variables related to sexuality. There is the need other research to explore how multiple factors, such as social, psychological, cultural, religious, ethnic, and ethical factors, affect sexuality. This promotes the creation of the paths for comprehensive care and management of patients.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.083
GPT teacher head0.496
Teacher spread0.414 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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