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Record W4385790448 · doi:10.1016/j.xkme.2023.100713

Female Reproductive Health and Contraception Use in CKD: An International Mixed-Methods Study

2023· article· en· W4385790448 on OpenAlexafffund
Danica H. Chang, Sandra M. Dumanski, Erin A. Brennand, Shannon M. Ruzycki, Kaylee Ramage, Taryn Gantar, Silvi Shah, Sofia B. Ahmed

Bibliographic record

VenueKidney Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsAlberta Kidney Disease NetworkLibin Cardiovascular Institute of AlbertaAlberta HealthUniversity of CalgaryUniversity of Alberta
FundersNational Heart, Lung, and Blood InstituteCanadian Institutes of Health ResearchFordham University
KeywordsMedicineDialysisKidney diseaseKidney transplantationFamily medicineTransplantationInternal medicineObstetricsGynecology

Abstract

fetched live from OpenAlex

Rationale & Objective: Female reproductive health is recognized as a predictor of morbidity, mortality, and quality of life, although data in the setting of chronic kidney disease (CKD) are limited. Study Design: A mixed-methods study was employed. Phase 1 was an anonymous, internet-based survey. Phase 2 was semistructured interviews offered to all respondents upon survey completion. Setting & Participants: The survey was disseminated internationally from October 4, 2021, to January 7, 2022, to individuals aged 18-50 years with both a uterus and CKD diagnosis. Outcomes: Menstrual health and contraceptive use by CKD stage (dialysis, nondialysis CKD, and transplant). Analytical Approach: Survey data were analyzed using descriptive statistics. Interview data were analyzed using the framework method of analysis. Results: Of 152 respondents, 98 (mean age 33 ± 0.7 years; n = 20 dialysis, n = 59 nondialysis CKD, n = 19 transplant) satisfied the inclusion criteria, representing 3 continents. The most common causes of CKD among survey respondents were hereditary causes in dialysis (n = 6, 30%) and glomerulonephritis in nondialysis CKD (n = 22, 37%) and transplant (n = 6, 32%). The majority reported heavy menstrual bleeding (n = 12, 86% dialysis; n = 46, 94% nondialysis CKD; n = 14, 100% transplant). Less than half of participants were consistently able to afford period products. Condoms were the most common contraceptive reported. Most participants reported no contraceptive use (n = 10, 50% dialysis; n = 37, 63% nondialysis CKD; n = 7, 37% transplant), primarily because of "fear". Interviews (n = 6) revealed a perception of a relationship between kidney function and menstrual health, concerns about contraceptive use, and a desire for greater multidisciplinary care to improve kidney and reproductive health. Limitations: Self-reported outcomes, need for internet access and a device. Conclusions: Abnormal menstruation and period poverty (ie, inability to afford period products and the socioeconomic consequences of menstruation) were common, and contraceptive use was low among female individuals with CKD, highlighting an important gap in the sex-specific care of this population. Plain-Language Summary: Chronic kidney disease (CKD) in female individuals is accompanied by menstrual disorders and low contraceptive use. However, most data are limited to the dialysis and transplant populations. Therefore, this mixed-methods study aimed to describe self-assessed menstruation and contraceptive use across all stages of CKD. People aged 18-50 years with a uterus and CKD diagnosis were invited to participate in an online survey shared internationally as well as an optional telephone interview. Abnormal menstruation and period poverty (ie, inability to afford period products and the socioeconomic consequences of menstruation) were common, and contraceptive use was low among female individuals with CKD, highlighting an important gap in the sex-specific care of this population.

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)0.008
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.091
GPT teacher head0.474
Teacher spread0.383 · 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 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

Citations12
Published2023
Admission routes2
Has abstractyes

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