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Record W4324385660 · doi:10.3899/jrheum.230139

Optimizing Care for Pregnancy in Rheumatic Diseases: Barriers and Potential Solutions

2023· letter· en· W4324385660 on OpenAlexvenueno aff
Madhuri H Radhakrishna, Sunitha Kayidhi, Vinod Ravindran

Bibliographic record

VenueThe Journal of Rheumatology · 2023
Typeletter
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRheumatic diseasePregnancyIntensive care medicineDisease controlPediatricsDiseaseObstetricsInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

With improved disease control, an increasing number of women with rheumatic diseases (RDs) are considering pregnancies. Though there are many international guidelines available for managing pregnancies in RD, the execution of a plan for such care is generally limited by several factors, leaving gaps between evidence and practice.1-4 In this issue of The Journal of Rheumatology , a multicenter survey by Tani et al on patient care pathways in pregnant women with rare and complex connective tissue disease is an important step in attempting to map existing care pathways.5 Data were collected from 69 centers across 21 countries. Ninety-one percent of centers provided services to pregnant women with RD by a multidisciplinary team (MDT) that included gynecologists/obstetricians (though formalized inclusion of other disciplines was present only in 30% of centers). Pre-pregnancy care was provided in a majority (96%) of these centers, and in 64%, this care was provided by community-based general practitioners. However, a formalized care pathway for these pregnant women was established only in 49% of centers, and there was lack of adequate use of appropriate checklists or predefined protocols during the pregnancy (20%) and postpartum (19%) phases. Likewise, the frequency of monitoring during the pregnancy and postpartum phases was also variable between centers. Heterogeneity between these centers was further evident in their ability to prescribe drugs compatible with pregnancy. Notwithstanding a lack of generalizability arising mainly out of the involvement of far more centers based in Europe (54 out of 69 centers, with 27 from Italy) and a sampling bias due to survey respondents mostly being specialists in reproductive rheumatology, the present study does hold up a mirror in front of us with regard to providing optimum care for pregnancy in RDs.5 Clinical care … Address correspondence to Dr. V. Ravindran, Centre for Rheumatology, Calicut 673009, Kerala, India. Email: drvinod12{at}gmail.com.

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)0.024
metaresearch head score (Gemma)0.062
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.024
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.062
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0050.003
Scholarly communication0.0080.007
Open science0.0040.011
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0060.001

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 head0.020
GPT teacher head0.284
Teacher spread0.264 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2023
Admission routes1
Has abstractyes

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