Evidence map›Paper›PMID 41632956›Full record

ArticleJournal of medical Internet research2026

Integrated Prediction System for Individualized Ovarian Stimulation and Ovarian Hyperstimulation Syndrome Prevention: Algorithm Development and Validation.

Jingjing Chen, Jianjuan Zhao, Huiyu Qiu, Yanhui Liu, Yunqi Zhang, Qicheng Sun, Yan Yi, Hongying Tang, Jing Zhao, Bin Xu and 8 more

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

18 authors.

Jingjing Chen *Department of Reproductive Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.ORCID https://orcid.org/0000-0002-1857-8498
Jianjuan Zhao *Digital Health Lab, Institute for Six-sector Economy, Fudan University, Shanghai, China.ORCID https://orcid.org/0009-0004-9899-5006
Huiyu QiuDepartment of Reproductive Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.ORCID https://orcid.org/0009-0005-4360-8408
Yanhui LiuReproductive Medicine Department, The Third Affiliated Hospital of Shenzhen University, Shenzhen, China.ORCID https://orcid.org/0000-0003-2498-1264
Yunqi ZhangDigital Health Lab, Institute for Six-sector Economy, Fudan University, Shanghai, China.ORCID https://orcid.org/0009-0002-3642-5126
Qicheng SunDigital Health Lab, Institute for Six-sector Economy, Fudan University, Shanghai, China.ORCID https://orcid.org/0009-0003-7728-8268
Yan YiDepartment of Reproductive Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.ORCID https://orcid.org/0000-0002-7948-5201
Hongying TangDepartment of Reproductive Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.ORCID https://orcid.org/0000-0001-6091-5569
Jing ZhaoDepartment of Reproductive Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.ORCID https://orcid.org/0000-0002-3658-3274
Bin XuDepartment of Reproductive Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.ORCID https://orcid.org/0000-0001-6338-4230
Qiong ZhangDepartment of Reproductive Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.ORCID https://orcid.org/0000-0002-2656-6577
Ge YangDivision of Neonatology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangdong, China.ORCID https://orcid.org/0000-0002-1480-1998
Hui LiDepartment of Reproductive Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.ORCID https://orcid.org/0000-0002-7516-7126
Junjie LiuReproductive Medicine Department, The Third Affiliated Hospital of Shenzhen University, Shenzhen, China.ORCID https://orcid.org/0009-0006-4916-5107
Zhongzhou YangSchool of Medicine, Sun Yat-sen University, Guangdong, China.ORCID https://orcid.org/0000-0002-1131-4158
Shaolin Liang *Digital Health Lab, Institute for Six-sector Economy, Fudan University, Shanghai, China.ORCID https://orcid.org/0009-0009-4965-1270
Yanping Li *Department of Reproductive Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.ORCID https://orcid.org/0000-0002-4193-7321
Jing Fu *Department of Reproductive Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.ORCID https://orcid.org/0009-0008-3172-1317

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAccurately predicting ovarian response and determining the optimal starting dose of follicle-stimulating hormone (FSH) remain critical yet challenging for effective ovarian stimulation. Currently, there is a lack of a comprehensive model capable of simultaneously forecasting the number of oocytes retrieved (NOR) and assessing the risk of early-onset moderate-to-severe ovarian hyperstimulation syndrome (OHSS).

objectiveThis study aimed to establish an integrated mode capable of forecasting the NOR and assessing the risk of early-onset moderate-to-severe OHSS across varying starting doses of FSH.

methodsThis prognostic study included patients undergoing their first ovarian stimulation cycles at 2 independent in vitro fertilization clinics. Automated classifiers were used for variable selection. Machine learning models (11 for NOR and 11 for OHSS) were developed and validated using internal (n=6401) and external (n=3805) datasets. Shapley additive explanation was applied for variable interpretation. The best-performing models were incorporated into a web-based prediction tool.

resultsFor NOR prediction, 17 variables were selected, with the gradient boosting regressor achieving the highest performance (internal dataset: R

conclusionsThis study introduces an integrated framework for predicting NOR and early-onset moderate-to-severe OHSS risk across different FSH doses. Future prospective evaluation is needed before clinical implementation.

Indexed as

AlgorithmsOvarian Hyperstimulation SyndromeOvulation InductionAdultBoosting Machine Learning AlgorithmsFemaleFollicle Stimulating HormoneHumansMachine LearningOocyte RetrievalPrediction AlgorithmsFollicle Stimulating Hormonefollicle-stimulating hormoneindividualized ovarian stimulationmachine learningovarian hyperstimulation syndromeovarian response

Identifiers

PMID41632956
PMCPMC12914236

What OpenQuestion holds

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Registered trials

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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.