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Record W4294578289 · doi:10.1016/j.rineng.2022.100613

Modelling epidural space heat transfer with air cooling via catheter insertion for spinal cord injury treatment

2022· article· en· W4294578289 on OpenAlexafffund
Nitin Seth, Michael D. Mohan, Dalya Al-Mfarej, Anne T. Nesathurai, Mostafa H. Sharqawy, Hussein A. Abdullah

Bibliographic record

VenueResults in Engineering · 2022
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of TorontoUniversity of GuelphUniversity of New Brunswick
FundersUniversity of Guelph
KeywordsEpidural spaceSpinal cordMedicineSpinal cord injuryCatheterAnesthesiaWater coolingSubdural spaceSurgeryMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Localized cooling of the spinal cord has been studied and demonstrated to have therapeutic and protective effects on traumatic spinal cord injury (TSCI). Often, the spinal cord is cooled using closed catheters systems that circulate a cooling fluid into the subdural or epidural space (ES) or alternatively, into the systemic circulation. Whether or not localized cooling of the epidural space can be done using an open catheter system with air, as the cooling medium remains an open question. This paper proposes a manual derivation for modelling the system dynamics of an air-cooled open catheter system to determine the feasibility of a low-cost design. Preliminary experimental testing is also performed to gather evidence of design effectiveness as a proof of concept. The results demonstrate that cooling of the ES can be achieved through routine surgical procedures allowing for the introduction of air into the spinal cord, thus providing cooling effects to assist in TSCI recovery.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.765

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)0.0000.000

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 head0.045
GPT teacher head0.328
Teacher spread0.282 · 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 designSimulation or modeling
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

Citations0
Published2022
Admission routes2
Has abstractyes

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