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Record W2777553623 · doi:10.14740/jocmr3307w

Different Effects of Oral Contraceptive and Dydrogesterone Treatment on Oxidative Stress Levels in Premenopausal Women

2017· article· en· W2777553623 on OpenAlexvenueno aff
Jui‐Tung Chen, Kazuhiko Kotani

Bibliographic record

VenueJournal of Clinical Medicine Research · 2017
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDydrogesteroneOxidative stressInternal medicineGastroenterologyGynecologyEstrogen

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of the study was to observe the changes in blood oxidative stress levels by oral contraceptive (OC) and/or dydrogesterone (DG) treatment. METHODS: A retrospective cohort of 27 premenopausal women with primary dysmenorrhea consisted of the OC treatment group (N = 17) and the DG treatment group (N = 10) by choice of the initial treatment. The OC group included two subgroups: patients with continuous OC treatment (treated for at least 15 months, N = 10) and patients with discontinuous OC treatment (switched to DG treatment after approximately 6 months of initial OC treatment: N = 7). The DG group had 15 months of continuous DG treatment. Blood parameters, including diacron-reactive oxygen metabolites (d-ROMs: an oxidative stress marker), were measured. RESULTS: The d-ROMs level was elevated in the OC group 3 months after initial treatment (mean: from 321 (at baseline) to 512 Carratelli Units (Carr U); P < 0.01), while such changes were not observed in the DG group. The d-ROMs level was reduced in the discontinuous OC subgroup 15 months after initial treatment (from 508 (3 months after initial treatment) to 372 Carr U; P < 0.01), while such changes were not observed in the continuous OC subgroup. The DG group displayed unchanged the d-ROMs level. CONCLUSION: Replacing OC with DG can attenuate oxidative stress as elevated by OC, thereby alleviating the possible vascular risks with OC treatment.

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)0.006
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.296
GPT teacher head0.566
Teacher spread0.270 · 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 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

Citations14
Published2017
Admission routes1
Has abstractyes

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