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Record W16777929 · doi:10.1093/aje/kwj214

Menopausa em (re)vista : os discursos praticados pela Revista Maria em torno da menopausa (1978-1988)

2010· dissertation· en· W16777929 on OpenAlexaboutno aff
Marília Regina de Azevedo Sousa Anjo

Bibliographic record

VenueAmerican Journal of Epidemiology · 2010
Typedissertation
Languageen
FieldSocial Sciences
TopicGender, Sexuality, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

There is widespread concern about possible long-term health effects among women who have received breast implants for cosmetic purposes; few studies have reported on the mortality patterns of such women. The authors examined cause-specific mortality in a cohort of 24,558 women with breast implants and 15,893 women who underwent other plastic surgery procedures in Ontario and Quebec, Canada, between 1974 and 1989. Deaths through 1997 were identified through linkage to the national mortality database. The authors compared the mortality of women who received implants with that of the general population by using standardized mortality ratios; Poisson regression was used to perform internal cohort comparisons. Overall mortality was lower among women who received breast implants relative to the general population (standardized mortality ratio = 0.74, 95% confidence interval: 0.68, 0.81). In contrast, higher suicide rates were observed in both the implant (standardized mortality ratio = 1.73, 95% confidence interval: 1.31, 2.24) and other plastic surgery (standardized mortality ratio = 1.55, 95% confidence interval: 1.07, 2.18) patients. No differences in mortality were found between the implant and other surgeries group for any of the 20 causes of death examined. Findings suggest that breast implants do not directly increase mortality in women. Further work is needed to evaluate risk factors for suicide among women who undergo elective cosmetic surgery.

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)0.013
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.059
GPT teacher head0.409
Teacher spread0.350 · 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.

Study designQualitative
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

Citations0
Published2010
Admission routes1
Has abstractyes

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