Evidence map›Paper›PMID 42876358›Full record

ReviewClinical and translational science2026

Clinical Decision Support Models to Optimize Controlled Ovarian Hyperstimulation: A Scoping Review.

Robin Z Y Lu, Toni J F Michael, Vinayak Smith, Beverley J Vollenhoven, Sophie L Stocker

Abstract readScoping ReviewReview
In one paragraph

Review in Clinical and translational science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Robin Z Y LuSchool of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.ORCID https://orcid.org/0009-0005-2865-4422
Toni J F MichaelSchool of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-6422-8683
Vinayak SmithVirtus Health Pty Ltd., Melbourne, Victoria, Australia.ORCID https://orcid.org/0000-0001-6099-3671
Beverley J VollenhovenSchool of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-8756-3994
Sophie L StockerSchool of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-2114-587X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Controlled ovarian hyperstimulation (COH) is a precision medicine challenge, with substantial inter-individual variability in ovarian response affecting treatment efficacy and safety during in vitro fertilization (IVF). Clinical decision support (CDS) tools incorporating statistical, machine learning, and artificial intelligence approaches have been developed to individualize gonadotropin dosing and ovulation trigger timing. Therefore, this study aims to identify and evaluate CDS tools informing prescribing decisions during COH. Following PRISMA-ScR guidance, Embase, MEDLINE, PubMed, and Scopus were searched from inception to March 2026. Studies evaluating CDS tools informing COH dosing and/or ovulation trigger timing during IVF were included, stratified by predictive domain and analyzed descriptively. Primary outcomes were oocyte yield and metaphase II (MII) oocyte yield. Secondary outcomes included gonadotropin use and pregnancy-related outcomes. Of 2879 records identified, five studies (n = 16,441) were included. One study reported precision-dosing CDS tools that were associated with increased MII oocyte yield (1.5 oocytes, p < 0.001) and reduced gonadotropin use (195 IU, p < 0.001) compared with clinician-directed dosing. A trigger-timing model improved MII oocyte yield (2.5 oocytes, p < 0.01), whereas combined dosing-and-trigger models demonstrated inconsistent effects. K-nearest neighbor (n = 3) and linear regression (n = 3) approaches predominated. No pregnancy-related outcomes were reported. No clinically evaluated mechanistic pharmacokinetic/pharmacodynamic models were identified. CDS tools show promise for individualizing COH and improving treatment efficiency through precision dosing. However, clinical translation remains limited by a small and heterogeneous evidence base, reliance on surrogate outcomes, and lack of mechanistic model-informed approaches. Prospective studies evaluating clinically meaningful outcomes are required to establish the role of CDS tools in personalized reproductive medicine.

Indexed as

Decision Support Systems, ClinicalFertilization in VitroOvulation InductionFemaleGonadotropinsHumansOocyte RetrievalOocytesPrecision MedicinePregnancyGonadotropinsclinical decision supportcontrolled ovarian hyperstimulationgonadotropinin vitro fertilizationmachine learningovulation triggerprecision dosingscoping review

Identifiers

PMID42876358
PMCPMC13670003

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.