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Record W2106692637 · doi:10.3109/10731190009118588

Assessment of Low-Power Laser Biostimulation on Chondral Lesions: An “In Vivo” Experimental Study

2000· article· en· W2106692637 on OpenAlexfundno aff
Gaetano Antonio Guzzardella, G Morrone, Paola Torricelli, M. Rocca, Domenico Tigani, Giovanni Barbanti Bròdano, Daniele Ferrari, Milena Fini, Roberto Giardino

Bibliographic record

VenueArtificial Cells Blood Substitutes and Biotechnology · 2000
Typearticle
Languageen
FieldMedicine
TopicLaser Applications in Dentistry and Medicine
Canadian institutionsnot available
FundersIstituto Ortopedico Rizzoli di BolognaUniversity of British Columbia
KeywordsLesionMedicineBiostimulationFEMORAL CONDYLELaserSurgeryNuclear medicineIn vivoAnatomyCartilage

Abstract

fetched live from OpenAlex

The purpose of this study was to evaluate whether intraoperative laser biostimulation can enhance healing of cartilaginous lesions of the knee. Surgery was performed on eighteen rabbits: a bilateral chondral lesion of 1.25 +/- 0.2 mm in length and 0.8 +/- 0.2 mm in width was created in the femoral medial condyle with a scalpel. The lesion in the left knee of each animal was treated intraoperatively using the diode Ga-Al-As 780nm. laser (300 Joules/cm2, 1 Watt, 300 Hertz, 10 minutes), while the right knee was left untreated, as control group. The animals were divided into three groups, A, B and C, according to the survival time after surgery, two, six and twelve weeks, respectively. The explants from the femoral condyles, both treated employing laser energy and left untreated, were examined histologically. Results showed a progressive filling with fibrous tissue of the cartilaginous lesion treated with laser irradiation, while no changes in the original lesion of the untreated group were observed at the end of the study. Maybe, in this experimental research, underexposure to laser irradiation was the cause for the absence of the necessary conditions for biostimulation.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

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)0.0010.000

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 head0.017
GPT teacher head0.314
Teacher spread0.297 · 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 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

Citations11
Published2000
Admission routes1
Has abstractyes

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