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Record W2009296356 · doi:10.1002/lsm.20493

Microtomographic analysis of healing of femtosecond laser bone calvarial wounds compared to mechanical instruments in mice with and without application of BMP‐7

2007· article· en· W2009296356 on OpenAlexaff
Bruno Girard, Martin Cloutier, David J. Wilson, Cameron M. L. Clokie, R. J. Dwayne Miller, Brian C. Wilson

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
KeywordsCalvariaFemtosecondWound healingLaserBiomedical engineeringBone healingMedicineDentistrySurgeryMaterials scienceChemistryOptics

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVE: This study investigated the healing of femtosecond laser created wounds in an animal model. STUDY DESIGN: We have assessed the healing of critical size wounds in mice calvaria using three different wounding techniques: carbide bur, diamond end-cutting bur, and ultrafast femtosecond laser, and in the presence or absence of bone morphogenetic protein-7 (BMP). Wound closure was examined using microcomputerized tomography at 3, 6, 9, and 12 weeks. RESULTS: Results have shown partial closure at up to 12 weeks with all techniques that did not involve the use of BMP, with the least closure noted in the laser groups as suggested by two-dimensional radiographic analysis. Bone volume measurements appeared slightly lower for the laser than for the mechanical groups, however statistically significant differences were seen only at week 6. No significant differences in closure were noted for the different methods in the BMP treated groups. CONCLUSIONS: Femtosecond laser cutting demonstrated an unsurpassed precision when compared to mechanical instruments. The addition of BMP led to very rapid healing with complete closure seen as early as 3 weeks and overcomes any potential healing delays that may arise from laser tissue cutting.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.035
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.016
GPT teacher head0.305
Teacher spread0.288 · 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.

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

Citations19
Published2007
Admission routes1
Has abstractyes

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