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ENHANCING INTERDISCIPLINARY COLLABORATION IN THE MANAGEMENT OF PREGNANCY IN PATIENTS WITH SYSTEMIC AUTOIMMUNE/AUTOINFLAMMATORY RHEUMATIC DISEASES: THE IMPACT OF JOINT RHEUMATOLOGY AND OBSTETRICS MEETINGS

2025· article· en· W4410513122 on OpenAlexvenueno aff
Masato Okada, Takehiro Nakai, Hiroki Ozawa

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRheumatologyPregnancyInternal medicinePhysical therapyObstetrics

Abstract

fetched live from OpenAlex

PV189 / #70 Poster Topic: AS21 - Pregnancy and Reproductive Health Background/Purpose Management of pregnancy in patients with systemic autoimmune/autoinflammatory rheumatic disease (SARD) requires close collaboration between rheumatologists and obstetricians. However, regular face-to face meeting is not held even academic institutions due to difficulties in scheduling and lack of sufficient participants from obstetric department due to less interests. Objectives Recognizing the need for increased collaboration, monthly interdepartmental meetings were initiated to share information and improve patient outcome. education for young clinicians. The primary goal was to make as needed communication easier and strengthen meticulous collaboration between the rheumatology and obstetrics departments to improve the management of pregnant patients with SARDs. Methods Joint meetings between the 2 departments were held at a tertiary medical facility. To continue the meeting with sufficient participants, 5 principles were established as follows; 1. Starting at 16:30 sharp on Wednesday and finish by 16:55. 2. Members of rheumatology department comes to Obstetric staff area in time. 3. All cases of infertility treatment, pregnancy, and delivery are discussed and to facilitate the discussion, a designated rheumatology fellow prepares 1 slide for each patient and present all cases. 4. Encouragement to participate on site but option of remote attendance via Microsoft teams is available. 5. Mini-lectures and confirmation of consensus in care are done at the end as far as time allows. At the end of FY2022, a survey of staff in both departments was conducted regarding these meetings. Based on the feedback from the survey, bi-directional mini-lectures on pregnancies complicated by RMDs were initiated starting in FY2023. Results In FY2022-2023, there were discussions on a total of 197 pregnancy cases, and 10 infertility treatment cases. SLE was the most common background disease among pregnant women, followed by rheumatoid arthritis and antiphospholipid antibody syndrome. There were no serious adverse pregnancy outcomes (APOs) for the mothers, and the 3 cases of premature birth had uneventful postnatal courses leading to discharge. Improvements in interdepartmental collaboration included 1) standardized protocols for corticosteroid coverage and 2) coordinated aspirin prescribing. According to the staff survey, 68.4% felt that their understanding of pregnancy management had deepened and 94.7% felt that interdepartmental collaboration had improved. Based on the results of the questionnaire, mini-lectures for mutual understanding were initiated; a total of 8 lectures were held in FY2023 on the most requested topics. Conclusions The implementation of joint meetings between rheumatology and obstetrics has greatly enhanced communication and is a critical step in patient care. In the context of progressive work style reforms, the sustained practice of these concise and effective meetings promises not only to deepen mutual understanding among professionals, but also to significantly improve the standard of care for our patients. Looking ahead, this collaborative model sets a promising precedent for interdisciplinary teamwork in health care.

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)0.012
metaresearch head score (Gemma)0.023
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0030.000
Scholarly communication0.0020.002
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.006
GPT teacher head0.276
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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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