Evidence map›Paper›PMID 41286145›Full record

ArticleScientific reports2025

Establishing correlation between bone mineral apposition rate and physiological loading using machine learning regressor.

Rakesh Kumar, Siddhanth Das, Davis Fernandes, Ashish Goyal

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. 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

4 authors.

Rakesh KumarDepartment of Mechanical Engineering, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India.
Siddhanth DasDepartment of Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India.
Davis FernandesDepartment of Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India.
Ashish GoyalDepartment of Mechanical Engineering, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India. ashish.goyal@jaipur.manipal.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ageing related post-menopausal osteoporosis is a global healthcare problem for orthopaedic clinicians. As age creeps in, the body tends to have an inhibition in the rate of estrogen production that negatively reflects on the bone mineral apposition rate (BMAR). The present study attempts to establish a correlation between the loading parameters such as strain magnitude, frequency, and the number of cycles on BMAR using Machine learning regressors namely Random Forest Regressor, Support Vector Machine Regressor, K-Nearest Neighbours Regressor, and XGBoost Regressor. The present model is trained and validated using the experimental data, which is later used to simulate the feature importance of loading parameters. XGBoost Regressor outperforms during the prediction of BMAR at the periosteal and endosteal surface. A strong correlation of R

Indexed as

Bone DensityMachine LearningFemaleHumansOsteoporosis, PostmenopausalSupport Vector MachineBone adaptationMachine learning regressorMineral apposition rateStrain

Identifiers

PMID41286145
PMCPMC12644685

What Socratic 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 Socratic graph.