Evidence map›Paper›PMID 37873224›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Large-scale characterization of gender differences in diagnosis prevalence and time to diagnosis.

Tony Yue Sun, Jill Hardin, Harry Reyes Nieva, Karthik Natarajan, Ru-Fong Cheng, Patrick Ryan, Noémie Elhadad

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. 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

7 authors.

Tony Yue SunDepartment of Biomedical Informatics, Columbia University; New York City, USA.
Jill HardinJanssen Research and Development; Titusville, USA.
Harry Reyes NievaDepartment of Biomedical Informatics, Columbia University; New York City, USA.
Karthik NatarajanDepartment of Biomedical Informatics, Columbia University; New York City, USA.
Ru-Fong ChengGender Equality Division, Bill and Melinda Gates Foundation; Seattle, USA.
Patrick RyanJanssen Research and Development; Titusville, USA.
Noémie ElhadadDepartment of Biomedical Informatics, Columbia University; New York City, USA.

Funding

Training in Biomedical Informatics at Columbia UniversityT15LM007079 · NLM · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI NOEMIE ELHADAD, GEORGE M HRIPCSAK · 1992 to 2026
$28.9M
NLM NIH HHS T15 LM007079
6 · The paper itself

Abstract

We carry out an analysis of gender differences in patterns of disease diagnosis across four large observational health datasets and find that women are routinely older when first assigned most diagnoses. Among 112 acute and chronic diseases, women experience longer lengths of time between symptom onset and disease diagnosis than men for most diseases regardless of metric used, even when only symptoms common to both genders are considered. These findings are consistent for patients with private as well as government insurance. Our analysis highlights systematic gender differences in patterns of disease diagnosis and suggests that symptoms of disease are measured or weighed differently for women and men. Data and code leverage the open-source common data model and analytic code and results are publicly available.

Identifiers

PMID37873224
PMCPMC10592987

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.