Evidence map›Paper›PMID 36643692›Full record

ReviewCurrent opinion in endocrine and metabolic research2022

Quantitative approaches in clinical reproductive endocrinology.

Margaritis Voliotis, Simon Hanassab, Ali Abbara, Thomas Heinis, Waljit S Dhillo, Krasimira Tsaneva-Atanasova

Open access · hybridAbstract readReview
In one paragraph

Review in Current opinion in endocrine and metabolic research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.1field-weighted citation impact, top 22% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
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

6 authors at 2 institutions in 1 country.

Margaritis VoliotisDepartment of Mathematics and Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom.
Simon HanassabSection of Endocrinology and Investigative Medicine, Imperial College London, London, United Kingdom.
Ali AbbaraSection of Endocrinology and Investigative Medicine, Imperial College London, London, United Kingdom.
Thomas HeinisDepartment of Computing, Imperial College London, London, United Kingdom.
Waljit S DhilloSection of Endocrinology and Investigative Medicine, Imperial College London, London, United Kingdom.
Krasimira Tsaneva-AtanasovaDepartment of Mathematics and Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom.
Imperial College London · GBUniversity of Exeter · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the human hypothalamic-pituitary-gonadal (HPG) axis presents a major challenge for medical science. Dysregulation of the HPG axis is linked to infertility and a thorough understanding of its dynamic behaviour is necessary to both aid diagnosis and to identify the most appropriate hormonal interventions. Here, we review how quantitative models are being used in the context of clinical reproductive endocrinology to: 1. analyse the secretory patterns of reproductive hormones; 2. evaluate the effect of drugs in fertility treatment; 3. aid in the personalization of assisted reproductive technology (ART). In this review, we demonstrate that quantitative models are indispensable tools enabling us to describe the complex dynamic behaviour of the reproductive axis, refine the treatment of fertility disorders, and predict clinical intervention outcomes.

Indexed as

AI, artificial intelligenceAMH, anti-Müllerian hormoneART, assisted reproductive technologyArtificial intelligenceAssisted reproductive technologyBSA, Bayesian Spectrum AnalysisClinical decision makingE2, estradiolFSH, follicle-stimulating hormoneGnRH, gonadotropin-releasing hormoneHA, hypothalamic amenorrheaHPG, hypothalamic-pituitary gonadalIn vitro fertilizationIVF, in vitro fertilizationLH, luteinizing hormoneMachine learningMathematical modellingML, machine learningOHSS, ovarian hyperstimulation syndromeP4, progesteronePCOS, polycystic ovary syndromePulsatility analysisQuantitative modellingReproductive endocrinology

Identifiers

PMID36643692
PMCPMC9831018
OpenAlexW4302363664

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.