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Record W2028900392 · doi:10.1097/bot.0b013e3181ca3b58

Nanotechnology and Bone Healing

2010· review· en· W2028900392 on OpenAlexafffund
Edward J. Harvey, Janet E. Henderson, Srikar Vengallatore

Bibliographic record

VenueJournal of Orthopaedic Trauma · 2010
Typereview
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsMcGill University Health CentreMcGill University
FundersCanadian Institutes of Health Research
KeywordsMedicineNanotechnologyBone healingTraumatologyBiomedical engineeringOrthopedic surgerySurgeryMaterials science

Abstract

fetched live from OpenAlex

Nanotechnology and its attendant techniques have yet to make a significant impact on the science of bone healing. However, the potential benefits are immediately obvious with the result that hundreds of researchers and firms are performing the basic research needed to mature this nascent, yet soon to be fruitful niche. Together with genomics and proteomics, and combined with tissue engineering, this is the new face of orthopaedic technology. The concepts that orthopaedic surgeons recognize are fabrication processes that have resulted in porous implant substrates as bone defect augmentation and medication-carrier devices. However, there are dozens of applications in orthopaedic traumatology and bone healing for nanometer-sized entities, structures, surfaces, and devices with characteristic lengths ranging from 10s of nanometers to a few micrometers. Examples include scaffolds, delivery mechanisms, controlled modification of surface topography and composition, and biomicroelectromechanical systems. We review the basic science, clinical implications, and early applications of the nanotechnology revolution and emphasize the rich possibilities that exist at the crossover region between micro- and nanotechnology for developing new treatments for bone healing.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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)0.0000.000

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 head0.021
GPT teacher head0.265
Teacher spread0.244 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations55
Published2010
Admission routes2
Has abstractyes

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