Evidence map›Paper›PMID 38389707›Full record

ReviewFrontiers in cell and developmental biology2024

PCOS stratification for precision diagnostics and treatment.

Anagha Joshi

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in cell and developmental biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 2 pooled it
17.7field-weighted citation impact, top 1% 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

27 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.

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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

1 author at 1 institution in 1 country.

Anagha JoshiComputational Biology Unit, Department of Clinical Science, University of Bergen, Bergen, Norway.
University of Bergen · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Globally, polycystic ovarian syndrome (PCOS) affects approximately 10% of fertile women, leading to great health and economic burden. PCOS is a heterogenous illness that can cause infertility, irregular menstrual cycles, acne, and hirsutism, among other symptoms. The clinical diagnosis is primarily a diagnosis of exclusion if one or more of the three primary symptoms, namely, oligo- or anovulation, hyperandrogenism, and polycystic ovarian morphology, are present. Obesity and PCOS are often coexisting disorders that may be bidirectionally causally related. Phenotypic heterogeneity throughout the reproductive lifespan, such as the overlap of PCOS symptoms with regular fluctuations in a woman's menstrual cycle and metabolism during the menarche and menopausal transition, further complicates diagnosis. PCOS etiology is mostly unknown and complex, likely due to the fact that it is a group of disorders with overlapping metabolic and reproductive problems. Evidence-based, common, standardized guidelines for PCOS diagnosis and treatment are urgently needed. Genomics and clinical data from populations across diverse ages and ethnicities are urgently needed to build efficient machine learning models for the stratification of PCOS. PCOS subtype-specific strategies for early screening, an accurate diagnosis, and management throughout life will optimize healthcare resources and reduce unnecessary testing. This will pave the way for women to be able to take the best possible care of their own health using the latest clinical expertise combined with their unique needs and preferences.

Indexed as

diagnosis, treatmentmachine learningPCOSstratificationwomen’s health

Identifiers

PMID38389707
PMCPMC10881805
OpenAlexW4391649323

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.