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Record W1972878727 · doi:10.1002/lsm.20466

Effects of femtosecond laser irradiation on osseous tissues

2007· article· en· W1972878727 on OpenAlexaff
Bruno Girard, Duo Yu, Michael R. Armstrong, Brian C. Wilson, Cameron M. L. Clokie, R. J. Dwayne Miller

Bibliographic record

VenueLasers in Surgery and Medicine · 2007
Typearticle
Languageen
FieldMedicine
TopicLaser Applications in Dentistry and Medicine
Canadian institutionsMount Sinai HospitalUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsLaserFemtosecondAblationIrradiationMaterials scienceConfocalBiomedical engineeringLaser ablationChemistryOpticsMedicine

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVE: Few studies have investigated femtosecond (fs) lasers for cutting bone tissue. STUDY DESIGN/MATERIALS AND METHODS: A 775 nm, 1 kHz, 200 femtosecond, up to 400 microJ laser system was used to irradiate in vitro calcified cortical bone samples and bone tissue culture samples. RESULTS: The ablation threshold in cortical bone was 0.69+/-0.08 J/cm(2) at 775 nm and 0.19+/-0.05 J/cm(2) at 387 nm. Plasma shielding experiments determined that the ablation plume and the plasma significantly affect material removal at high repetition rates and appear to generate thermal transients in calcified tissue. Confocal analysis revealed intact enzymatic activity on the surface of cells immediately adjacent to cells removed by fs laser irradiation. CONCLUSIONS: These experiments demonstrate that fs lasers used for bone tissue cutting do not appear to generate significant temperature transients to inactivate proteins and that cellular membrane integrity is disrupted for only a few cell layers.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

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)0.0020.000

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 head0.012
GPT teacher head0.298
Teacher spread0.286 · 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 designBench or experimental
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

Citations82
Published2007
Admission routes1
Has abstractyes

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