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Record W4223538260 · doi:10.52547/jcc.4.1.6

Surface modification of metallic orthopedic implants for anti-pathogenic characteristics

2022· article· en· W4223538260 on OpenAlexaff
Varinder Pal Singh Sidhu, Juliana Marchi, Roger Borges, Elahe Naghizadeh Raeisi

Bibliographic record

VenueJournal of Composites and Compounds · 2022
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Infections and Treatments
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsOrthopedic surgeryAntibioticsAntimicrobialMedicinePathogenic bacteriaAntibiotic resistanceMicrobiologyBiologySurgeryBacteria

Abstract

fetched live from OpenAlex

Bacterial infection is one of the main reasons for the long-term failure of orthopedic implants. Despite remarkable progression in antimicrobial drugs, implant-associated infection (IAI) remains difficult to treat, which is resulted from bacterial resistance against antibiotics. As a result, there is an urgent need to develop alternative approaches. The present review highlights surface modification of the orthopedic implants as a promising approach to inhibit bacterial infection. This approach can be classified into two groups: (1) bacteriostatic (anti-adhesive), and (2) bactericidal (contact-killing/release-killing) surfaces. Their combination, which is considered as bacteriostatic-bactericidal bi-functional surface, can provide a more robust approach against dangerous pathogenic species. New approaches and future perspectives in this inspiring field are also provided.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.023
GPT teacher head0.287
Teacher spread0.264 · 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 teacher head, 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

Citations6
Published2022
Admission routes1
Has abstractyes

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