Evidence map›Paper›PMID 38933390›Full record

ReviewFundamental research2022

Medical care of rare and undiagnosed diseases: Prospects and challenges.

Zhiyan Shan, Lijun Ding, Caiyun Zhu, Ruijuan Sun, Wei Hong

Abstract readReview
In one paragraph

Review in Fundamental research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Multimodality treatment and survival in primary cardiac malignancies: evidence from competing-risk and machine learning analyses.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Article
  2. Review
  3. Review
  4. Article
  5. 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

5 authors.

Zhiyan ShanDepartment of Health Sciences, National Natural Science Foundation of China, Beijing 100085, China.
Lijun DingDepartment of Health Sciences, National Natural Science Foundation of China, Beijing 100085, China.
Caiyun ZhuState Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China.
Ruijuan SunDepartment of Health Sciences, National Natural Science Foundation of China, Beijing 100085, China.
Wei HongDepartment of Health Sciences, National Natural Science Foundation of China, Beijing 100085, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rare and undiagnosed diseases tend to be diverse, misdiagnosed, and difficult to diagnose. In some cases, the disease is progressive and life-threatening. Yet, to date, an estimated 95% of rare diseases have no approved therapy. Therefore, rare and undiagnosed diseases are considered the ultimate challenges for understanding human diseases. Here, we review the research progress, research frontiers, and important scientific issues related to rare and undiagnosed diseases. We mainly focus on five topics: (1) the identification and functional analysis of disease-causing genes; (2) the construction of cells, organoids, and animal models for mechanism validation; (3) subtyping and diagnosis; (4) treatment and drug screening based on causative genes and mutations; and (5) new technologies and methods for studying rare and undiagnosed diseases. In this review, we briefly update and discuss the pathogenic mechanisms and precision medicine for rare and undiagnosed diseases.

Indexed as

Animal modelDiagnosisGeneRare diseaseTherapyUndiagnosed disease

Identifiers

PMID38933390
PMCPMC11197738

What OpenQuestion holds

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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.