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Risk of Pseudotumor Cerebri Syndrome (PTCS) with hormonal contraceptive use

2018· article· en· W2792935105 on OpenAlexafffund
Mohit Sodhi, Claire A. Sheldon, Bruce Carleton, Mahyar Etminan

Bibliographic record

VenueInternational Journal of Reproduction Contraception Obstetrics and Gynecology · 2018
Typearticle
Languageen
FieldMedicine
TopicCerebral Venous Sinus Thrombosis
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersProvincial Health Services Authority
KeywordsMedicineMedical prescriptionHormonal contraceptionDiagnosis codePseudotumor cerebriCohortConfidence intervalInternal medicineIncidence (geometry)ProgestinPediatricsPopulationObstetricsGynecologyHormoneSurgeryFamily planning

Abstract

fetched live from OpenAlex

Background: Hormonal contraceptives (HC), one of the most prescribed classes of medication in women, have been linked with pseudotumor cerebri syndrome (PTCS). To date, no large epidemiologic study has examined this association.Methods: A case-control study using the IMS LifeLink Pharmetrics Plus database was conducted. Cases had an ICD-9-CM code for benign intracranial hypertension as well as a procedural code for a CT or MRI and a code for lumbar puncture procedure within 15 days of the PTCS code. Controls were selected from the cohort using density-based sampling.Results: From a cohort of 9,053,240 subjects, there were 288 cases of PTCS corresponding to 2,880 controls. The adjusted RRs for two or more prescriptions of oral combined contraceptive was 0.62 (95% confidence interval 0.39-0.99). RRs for overall HC use was 0.91 (95% CI 0.39-2.12) for one prescription of HCs and 0.69 (95% CI 0.45-1.05) for two or more prescriptions. The RRs for one and two or more prescriptions of progestin only HCs were 0.75 (95% CI 0.08-7.46) and 1.06 (95% CI 0.42-2.69), respectively.Conclusions: Overall HC use does not have a significant effect on incidence of PTCS, however harm associated with progestin-only contraceptives cannot be excluded.

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)0.001
metaresearch head score (Gemma)0.009
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 score0.214
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.263
Teacher spread0.248 · 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

Citations4
Published2018
Admission routes2
Has abstractyes

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Same venueInternational Journal of Reproduction Contraception Obstetrics and GynecologySame topicCerebral Venous Sinus ThrombosisFrench-language works237,207