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Record W2979621561

Combined hormonal contraceptives use and bone mineral density changes in adolescent and young women in a prospective population-based Canada-wide observational study.

2018· article· en· W2979621561 on OpenAlexaffabout
Tatjana Subotic Brajic, Claudie Berger, Katharina Schlammerl, Heather Macdonald, Shirin Kalyan, David A. Hanley, Jonathan D. Adachi, Christopher S. Kovács, Jerilynn C. Prior

Bibliographic record

VenuePubMed · 2018
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineFemoral neckBone mineralMenarchePopulationOsteoporosisBone densityVitamin D and neurologyInternal medicineGynecology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVES: To assess combined hormonal contraceptives (CHC) use and adolescent women's peak areal bone mineral density (BMD) accrual. METHODS: We enrolled 527 randomly selected women across Canada (2004-6) divided by age into adolescents (16-19) and young adults (20-24) and by CHC use to ever (E-CHC)/never (N-CHC) users. At baseline and year 2 we measured height, weight, and BMD at lumbar spine (L1-4), femoral neck, and total hip sites. Interviewer-administered questionnaires addressed menarche age, cigarette and alcohol use, calcium/vitamin D intakes, physical activity and estrogen dose (≤30/>30 micrograms). Linear regression models examined associations of CHC use with 2-year BMD change adjusted for bone-related variables. RESULTS: (95%CI: -0.006; -0.036) with similar trends at other sites. BMD changes were unrelated to estrogen dose and age at starting CHC. CONCLUSIONS: Adolescent CHC users in a random population demonstrated less hip region peak BMD accrual than non-users. This requires randomized control trial confirmation.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.064
GPT teacher head0.290
Teacher spread0.226 · 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

Citations30
Published2018
Admission routes2
Has abstractyes

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Same venuePubMed→Same topicBone health and osteoporosis research→French-language works237,207→