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Record W2592066491 · doi:10.2316/p.2017.852-028

Instrumented Spinal Cord Surrogate Using Optical Fiber: Role of the Fiber's Location

2017· article· en· W2592066491 on OpenAlexaff
Yann Facchinello, Éric Wagnac, Bora Ung, Yvan Petit, Prabin Pradhan, Louis-Marie Peyrache, Jean‐Marc Mac‐Thiong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsÉcole de Technologie SupérieureHôpital du Sacré-Cœur de Montréal
Fundersnot available
KeywordsSpinal cordSpinal cord injuryFiberMaterials scienceCompression (physics)Optical fiberMedicineComputer scienceComposite materialTelecommunications

Abstract

fetched live from OpenAlex

In vitro replication of traumatic spinal cord injury is necessary to understand its biomechanics and to improve prevention devices and care. During the trauma, the spinal cord withstands an impaction at high velocity. In order to fully assess spinal cord compression, a physical spinal cord surrogate instrumented with bare optical fibers is a promising avenue. The sensing is based on light transmission loss observed in optical fibers subjected to bending. In this paper, the role of the fibers' position within the surrogate is presented and discussed. The closer the fiber from the surface of the spinal cord, the more sensitive it is to small compression magnitude. A threshold value of 15 % was observed for the fiber located at the upper surface of the surrogate. However, this fiber was not able to record compression over 40 %. Another fiber located in the middle of the surrogate was needed to record compression between 40 % to 85 %. Using two fibers allows continuous recording of compressions ranging from 15 % to 85 %. This study shows the relevance of using multiple fibers in order to fully assess the compression of the spinal cord surrogate.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.075
GPT teacher head0.405
Teacher spread0.331 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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