Evidence map›Paper›PMID 39610523›Full record

ReviewJournal of diabetes and metabolic disorders2024

Assessment of fracture risk in diabetic patients.

Zhenpeng Wang, Mei Zhang, Dan Jia

Abstract readReview
In one paragraph

Review in Journal of diabetes and metabolic disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Zhenpeng WangDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.
Mei ZhangDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.
Dan JiaDepartment of Outpatient, West China Hospital, Sichuan University, Chengdu, China.ORCID https://orcid.org/0009-0004-0226-5739

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Patients with diabetes often experience reduced bone strength, resulting in a higher fracture risk. This decline and increased susceptibility stem from intricate interactions within the bone microstructure. However, current gold standard methods for assessing bone strength, such as bone mineral density, and widely-used fracture risk assessment tools do not accurately predict fracture risk in diabetic patients. Therefore, it is crucial to incorporate additional indicators that evaluate bone quality and specific markers relevant to diabetes to enhance the accuracy of predictive models. Moreover, the selection of appropriate algorithms for model construction is essential. This review aims to introduce indicators from both imaging examinations and laboratory indicators that hold significant value for inclusion in fracture risk prediction models for diabetic patients. Additionally, this study provides an overview of the research progress in fracture risk prediction models for diabetic patients, serving as a valuable reference for clinical practice.

Indexed as

Diabetic patientsFracture riskMachine learningPrediction model

Identifiers

PMID39610523
PMCPMC11599524

What Socratic holds

Textmetadata
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.