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Record W2165228219 · doi:10.2174/1874325001004010221

Significance of Elevated Blood Metal Ion Levels in Patients with Metal-on-Metal Prostheses: An Evaluation of Oxidative Stress Markers

2010· article· en· W2165228219 on OpenAlexaff
C. Tkaczyk, Alain Petit, John Antoniou, David J. Zukor, Maryam Tabrizian, Olga L. Huk

Bibliographic record

VenueThe Open Orthopaedics Journal · 2010
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsJewish General HospitalMcGill University
Fundersnot available
KeywordsOxidative stressSuperoxide dismutaseReactive oxygen speciesCatalaseAntioxidantMetal ions in aqueous solutionMedicineGlutathione peroxidaseGlutathioneOxidative phosphorylationMetalCobaltBiochemistryInternal medicineEnzymeChemistryInorganic chemistry

Abstract

fetched live from OpenAlex

It is widely known that cobalt and chromium ions can enhance the production of reactive oxygen species, known to be damaging to cells by disturbing their redox status and then generating oxidative stress. The aim of the present study was to determine if increased metal ion levels induce a state of oxidative stress in patients with metal-on-metal (MM) hip arthroplasty. Results indicated that there was no significant difference in the concentration of oxidative stress markers (total antioxidants, peroxides, and nitrated proteins) in the patients with MM bearings compared to patients without prostheses. The activity antioxidant enzymes was stable (catalase and glutathione peroxidase) or slightly decreased (superoxide dismutase and heme oxygenase-1) over time. This work is the first to determine the biological effects of metal ions released from MM hip implants with regards to mid-term systemic oxidative stress and showed that the increased levels of Co and Cr ions are not associated with significant oxidative stress damage in the plasma of patients with these implants.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.033
GPT teacher head0.303
Teacher spread0.270 · 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

Citations29
Published2010
Admission routes1
Has abstractyes

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