Evidence map›Paper›PMID 39410466›Full record

ArticleMaterials (Basel, Switzerland)2024

Optimization Design and SLM Manufacturing of Porous Titanium Alloy Femoral Stem.

Lisong Zhao, Yukang Wang, Qing Wang, Yongdi Zhang, Guang Yang

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Application and prospects of nanomaterials in osteoporosis treatment.International journal of pharmaceutics: X · 2026
    Review
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.

Lisong ZhaoCollege of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China.
Yukang WangCollege of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China.
Qing WangCollege of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China.
Yongdi ZhangCollege of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China.
Guang YangCollege of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China.

Funding

Financial support for this study was provided by the Hebei Natural Science Foundation E2017208128
6 · The paper itself

Abstract

In order to solve the loosening problem caused by stress shielding of femoral stem prostheses in clinical practice, an optimization design method of a personalized porous titanium alloy femoral stem is proposed. According to the stress characteristics of the femur, the porous unit cell structures (TO-C, TO-T, TO-B) under three different loads of compression, torsion, and bending were designed by topology optimization. The mechanical properties and permeability of different structures were studied. Combined with the porous structure optimization, a personalized radial gradient porous titanium alloy femoral stem was designed and manufactured by selective laser melting (SLM) technology. The results show that the TO-B structure has the best comprehensive performance among the three topologically optimized porous types, which is suitable for the porous filling structure of the femoral stem, and the SLM-formed porous femoral stem has good quality. The feasibility of the personalized design and manufacture of porous titanium alloy implants is verified, which can provide a theoretical basis for the optimal design of implants in different parts.

Indexed as

femoral stemmechanical propertiespermeabilityporous titanium alloyselective laser meltingtopology optimization

Identifiers

PMID39410466
PMCPMC11477622

What OpenQuestion holds

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Registered trials

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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.