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Record W4324113066 · doi:10.12775/jehs.2023.14.01.002

The impact of aesthetic medicine procedures on patient comfort in life, physical activity and mental health

2023· article· en· W4324113066 on OpenAlexaff
Aleksandra Karwańska, Aleksandra Kulbat, Kamila Matyka, Izabela Uniłowska, Ewa KOJDER, Arsen DOLENHA, Yuliia DOVZHUK, Justyna OCHAŁ, Anna Ferschke, Aleksandra Szymczyk

Bibliographic record

VenueJournal of Education Health and Sport · 2023
Typearticle
Languageen
FieldPsychology
TopicBody Image and Dysmorphia Studies
Canadian institutionsLouisiana-Pacific (Canada)
Fundersnot available
KeywordsMental healthQuality of life (healthcare)Socioeconomic statusScholarshipAffect (linguistics)Sports medicineMedicineMEDLINEQuality (philosophy)PsychologyGerontologyNursingPhysical therapyPsychiatryEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

Introduction Aesthetic medicine procedures significantly affect the comfort of patient's lives, their amount of physical activity and mental health. Today, medicine serves not only to eliminate the consequences of diseases or their prevention, but also aesthetic considerations, which are directly related to the quality of life in general. Purpose The purpose of this review is to present the current state of knowledge on the impact of aesthetic medicine procedures on the comfort of patients' lives. Methods Literature was searched in PubMed and Google Scholarship databases. Publicly available books were searched. Results People who use aesthetic medicine can improve their self-esteem and even their socioeconomic status or get a better job than before. Conclusions Patients’s quality of life and mental health significantly increase after aesthetic medicine procedures.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.031
GPT teacher head0.431
Teacher spread0.399 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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