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Red blood cell and platelet interactions with titanium implant surfaces

2000· article· en· W2016576220 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueClinical Oral Implants Research · 2000
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsImplantFibrinPlateletTitaniumBiomedical engineeringMaterials scienceOsseointegrationPlatelet-rich fibrinDentistryBlood cellRed blood cellChemistrySurgeryMedicineMetallurgyImmunologyBiochemistry

Abstract

fetched live from OpenAlex

The influence of the micro-roughened surface, produced by dual acid-etching (DAE) of machined commercially pure titanium, on initial blood cell/implant interactions was investigated by observing the blood components remaining at the implant surface following freeze-fracture of clotted, and fixed, human blood. Glass surfaces were also used for immunolabelling studies to identify fibrin and platelets. The interface comprised predominantly fibrin and red blood cells (RBCs). The difference in distribution of RBCs was statistically significant (P < 0.05) at 10 min of blood/implant contact, but diminished thereafter. Micro-roughened DAE implant surfaces showed, qualitative, more platelets than machined surfaces, while the textured glass surfaces demonstrated increased platelet aggregation. We believe that these early blood cell/implant interactions may play a key role in the osteoconduction stage of peri-implant bone healing response to micro-roughened 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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.093
GPT teacher head0.391
Teacher spread0.298 · 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