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Record W2057495720 · doi:10.1364/boe.6.000043

Development of an integrated optical coherence tomography-gas nozzle system for surgical laser ablation applications: preliminary findings of in situ spinal cord deformation due to gas flow effects

2014· article· en· W2057495720 on OpenAlexaff
Ronnie Wong, Jamil Jivraj, Barry Vuong, Joel Ramjist, Nicole A. Dinn, Cuiru Sun, Yize Huang, James A. Smith, Victor X. D. Yang

Bibliographic record

VenueBiomedical Optics Express · 2014
Typearticle
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsSunnybrook Health Science CentreToronto Metropolitan University
Fundersnot available
KeywordsOptical coherence tomographyContext (archaeology)Biomedical engineeringAblationMaterials scienceCharringMedicineRadiologyComposite materialGeology

Abstract

fetched live from OpenAlex

Gas assisted laser machining of materials is a common practice in the manufacturing industry. Advantages in using gas assistance include reducing the likelihood of flare-ups in flammable materials and clearing away ablated material in the cutting path. Current surgical procedures and research do not take advantage of this and in the case for resecting osseous tissue, gas assisted ablation can help minimize charring and clear away debris from the surgical site. In the context of neurosurgery, the objective is to cut through osseous tissue without damaging the underlying neural structures. Different inert gas flow rates used in laser machining could cause deformations in compliant materials. Complications may arise during surgical procedures if the dura and spinal cord are damaged by these deformations. We present preliminary spinal deformation findings for various gas flow rates by using optical coherence tomography to measure the depression depth at the site of gas delivery.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.265
Teacher spread0.253 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

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