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Record W1972878727 · doi:10.1002/lsm.20466

Effects of femtosecond laser irradiation on osseous tissues

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

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

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.

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)0.001
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 score0.370
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.012
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