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Assessment of prolonged tissue response to porousiron implant by radiodensity approach

2015· article· en· W2289894074 on OpenAlexfundno aff
Sitaria Fransiska Siallagan, Gunanti, Arlita Sariningrum, Devi Paramitha, Mokhamad Fakhrul Ulum, Hendra Hermawan, Deni Noviana

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsnot available
FundersNational Institutes of HealthUniversité Laval
KeywordsRadiodensityRadiographyImplantMaterials scienceBiomedical engineeringBone tissueMedicineIn vivoDentistryRadiologySurgery

Abstract

fetched live from OpenAlex

Porous iron has been recently introduced as new biomaterials for bone scaffolds. As a metal that degrades in the in vivo setting (biodegradable metal), little has been known for its cell-material interaction within the living tissue especially in the view of its applications for bone implant materials. Therefore, the aim of this study is to assess tissue response to the implantation of porous iron in the in vivo setting using rats as animal model and radiographic examination. Ten adult Sprague Dawley rats received the implantation of porous iron implants having different porosity into their femoral bone followed by radiographic examination up to 5 months post-implantation. Results shows differences in the opacity of radiographic images where the implants looked more radiopaque than bone and muscle. Radiodensity values of the implants decreases overtime indicating they experienced progressive degradation. This values dynamically change over time up to 5 months in relation to the event of chronic inflammation, degradation and bone healing process.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.311
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2015
Admission routes1
Has abstractyes

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