Evidence map›Paper›PMID 31860990›Full record

ArticleMedicine2019

Gender- and age-dependent tongue features in a community-based population.

Po-Chi Hsu, Han-Kuei Wu, Yu-Chuen Huang, Hen-Hong Chang, Yi-Ping Chen, John Y Chiang, Lun-Chien Lo

Open access · goldAbstract read
In one paragraph

Article in Medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
2.9field-weighted citation impact, top 10% 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

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Pooled it
  2. Circadian Rhythms in Tongue Features.Journal of clinical medicine · 2024
    Article
  3. Article
  4. Accurate estimation of biological age and its application in disease prediction using a multimodal image Transformer system.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Mining intrinsic information of convalescent patients after suffering coronavirus disease 2019 in Wuhan.Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan · 2022
    Article
  12. Article
  13. Article
  14. Article
  15. A Perspective on Tongue Diagnosis in Patients with Breast Cancer.Evidence-based complementary and alternative medicine : eCAM · 2021
    Article
  16. 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

7 authors at 4 institutions in 2 countries.

Po-Chi HsuSchool of Chinese Medicine, China Medical University.
Han-Kuei WuSchool of Post-Baccalaureate Chinese Medicine, China Medical University, Taichung.
Yu-Chuen HuangSchool of Chinese Medicine, China Medical University.
Hen-Hong ChangDepartment of Chinese Medicine, China Medical University Hospital.
Yi-Ping ChenDepartment of Medical Research, China Medical University Hospital, Taichung.
John Y ChiangDepartment of Computer Science and Engineering, National Sun Yat-Sen University, Kaohsiung.
Lun-Chien LoSchool of Chinese Medicine, China Medical University.
China Medical University · CNChina Medical University · TWChina Medical University Hospital · TWNational Sun Yat-sen University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study, an important groundwork for clinical tongue diagnosis and future traditional Chinese medicine (TCM) research, tested the hypothesis that some tongue features vary significantly between different gender and age groups by utilizing an automatic tongue diagnosis system (ATDS).A cross-sectional study of 1487 participants from a community-based population was performed. Study subjects with ages ranging from 20 to 92 were categorized into 3 groups: <40, 40 to 64, and ≥65 years old, and the subjects were also stratified according to gender. Tongue images were collected at the end of each normal health examination routine to further derive the relevant tongue features of every participant by using the ATDS developed by our team. There were a total of nine tongue features that were identified: tongue shape, tongue color, fur thickness, fur color, saliva, tongue fissure, ecchymosis, teeth mark, and red dot. The corresponding tongue features, demography, and physical/laboratory examination data were compared between different gender and age groups.Our study showed that, compared to females, males had enlarged tongue shape, thicker fur, more fissures and fewer teeth marks (all P < .001), and also had more red tongue color (P = .019), normal saliva (P = .001), more red dots (P = .005) and yellower fur (P = .014). In females, increasing age was associated with more enlarged tongue shape, thicker fur, yellower fur, more saliva, fissures and fewer teeth marks (all P < .001), more ecchymoses (P = .009), and more red tongue color (P = .023). These associations of age with more fissures, fewer teeth marks, fewer red dots (P < .001), median tongue shape (P = .029), and wet saliva (P = .014) were also evident in males, but other relationships were not clearly evident.Even though most of the common tongue features derived from a community-based population are consistent with TCM theory, yet some significantly gender- and age-dependent tongue characteristics were identified. These disparities in tongue features associated with gender or age shall be prudently taken into consideration in clinical tongue diagnosis and future TCM research.

Indexed as

AdultAgedAged, 80 and overAgingCross-Sectional StudiesFemaleHumansMaleMedicine, Chinese TraditionalMiddle AgedSex CharacteristicsTaiwanTongueYoung Adult

Identifiers

PMID31860990
PMCPMC6940112
OpenAlexW2994839149

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.