Evidence map›Paper›PMID 33147345›Full record

ArticleHuman reproduction (Oxford, England)2021

Streamlining follicular monitoring during controlled ovarian stimulation: a data-driven approach to efficient IVF care in the new era of social distancing.

I Robertson, F P Chmiel, Y Cheong

Open access · greenAbstract read
In one paragraph

Article in Human reproduction (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 16% of its field
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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.

  1. Application of artificial intelligence to ultrasound imaging for benign gynecological disorders: systematic review.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2025
    Pooled it
  2. Article
  3. Article
  4. Article
  5. Monitoring of controlled ovarian stimulation in IVF.Journal of assisted reproduction and genetics · 2024
    Article
  6. Review
  7. Article
  8. 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

3 authors at 1 institution in 1 country.

I RobertsonHuman Development and Health, University of Southampton, Southampton SO16 5YA, UK.
F P ChmielIT Innovation Centre, School of Electronics and Computer Science, University of Southampton, Southampton SO16 7NS, UK.
Y CheongHuman Development and Health, University of Southampton, Southampton SO16 5YA, UK.
University of Southampton · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

study questionWhat is the optimal follicular tracking strategy for controlled ovarian stimulation (COS) in order to minimise face-to-face interactions? SUMMARY ANSWER: As data from follicular tracking scans on Days 5, 6 or 7 of stimulation are the most useful to accurately predict trigger timing and risk of over-response, scans on these days should be prioritised if streamlined monitoring is necessary. WHAT IS KNOWN ALREADY: British Fertility Society guidance for centres restarting ART following coronavirus disease 2019 (COVID-19) pandemic-related shutdowns recommends reducing the number of patient visits for monitoring during COS. Current evidence on optimal monitoring during ovarian stimulation is sparse, and protocols vary significantly. Small studies of simplifying IVF therapy by minimising monitoring have reported no adverse effects on outcomes, including live birth rate. There are opportunities to learn from the adaptations necessary during these extraordinary times to improve the efficiency of IVF care in the longer term. STUDY DESIGN, SIZE, DURATION: A retrospective database analysis of 9294 ultrasound scans performed during monitoring of 2322 IVF cycles undertaken by 1875 women in a single centre was performed. The primary objective was to identify when in the IVF cycle the data obtained from ultrasound are most predictive of both oocyte maturation trigger timing and an over-response to stimulation. If a reduced frequency of clinic visits is needed due to COVID-19 precautions, prioritising attendance for monitoring scans on the most predictive cycle days may be prudent. PARTICIPANTS/MATERIALS, SETTING,

methodsThe study comprised anonymised retrospective database analysis of IVF/ICSI cycles at a tertiary referral IVF centre. Machine learning models are used in combining demographic and follicular tracking data to predict cycle oocyte maturation trigger timing and over-response. The primary outcome was the day or days in cycle from which scan data yield optimal model prediction performance statistics. The model for predicting trigger day uses patient age, number of follicles at baseline scan and follicle count by size for the current scan. The model to predict over-response uses age and number of follicles of a given size. MAIN RESULTS AND THE ROLE OF CHANCE: The earliest cycle day for which our model has high accuracy to predict both trigger day and risk of over-response is stimulation Day 5. The Day 5 model to predict trigger date has a mean squared error 2.16 ± 0.12 and to predict over-response an area under the receiver operating characteristic curve 0.91 ± 0.01. LIMITATIONS, REASONS FOR CAUTION: This is a retrospective single-centre study and the results may not be generalisable to centres using different treatment protocols. The results are derived from modelling, and further clinical validation studies will verify the accuracy of the model. WIDER IMPLICATIONS OF THE

findingsFollicular tracking starting at Day 5 of stimulation may help to streamline the amount of monitoring required in COS. Previous small studies have shown that minimal monitoring protocols did not adversely impact outcomes. If IVF can safely be made less onerous on the clinic's resources and patient's time, without compromising success, this could help to reduce burden-related treatment drop-out. STUDY FUNDING/COMPETING INTEREST(S): F.P.C. acknowledges funding from the NIHR Applied Research Collaboration Wessex. The authors declare they have no competing interests in relation to this work. TRIAL REGISTRATION NUMBER: N/A.

Indexed as

Physical DistancingAdultCOVID-19FemaleHumansMachine LearningModels, BiologicalOocyte RetrievalOocytesOvarian FollicleOvulation InductionRetrospective StudiesSARS-CoV-2Time FactorsTime-to-TreatmentUltrasonographymachine learningoocyte maturationovarian stimulationover-responsetriggerultrasound

Identifiers

PMID33147345
PMCPMC7665450
OpenAlexW3089880423

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.