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Record W2963490373 · doi:10.1109/jtehm.2019.2919029

Defining the Relationship Between Compressive Stress and Tissue Trauma During Laparoscopic Surgery Using Human Large Intestine

2019· article· en· W2963490373 on OpenAlexafffund
Amanda Farah Khan, Matthew Kenneth MacDonald, Catherine Streutker, Corwyn Rowsell, James M. Drake, Teodor Grantcharov

Bibliographic record

VenueIEEE Journal of Translational Engineering in Health and Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicIntestinal and Peritoneal Adhesions
Canadian institutionsSt. Michael's HospitalHospital for Sick ChildrenMuscular Dystrophy CanadaUniversity of Toronto
FundersHospital for Sick Children
KeywordsMedicineCompression (physics)Compressive strengthPerforationAdhesionPathologicalSepsisSurgeryPathologyMaterials science

Abstract

fetched live from OpenAlex

Excessive magnitudes of compressive stress exerted on gastrointestinal tissues can lead to pathological scar tissue or adhesion formation, bleeding, inflammation or even death from bowel perforation and sepsis. It is currently unknown however, at exactly what magnitude of compressive stress that these pathologies occur. A novel simple compressive device was engineered to provide an objective means of producing discrete compressive stresses on human tissues. Samples of human large intestine (colon) were removed from consenting patients as a part of their standard surgical procedure. These samples were compressed with a range of loads normally produced by standard laparoscopic graspers in representative abdominal surgeries. After compression, specimens were processed for histological analysis and assessed. The two independent pathologists who were blinded to stress magnitudes were both able to quantify increasing tissue damage that corresponded to increasing amounts of compressive force. A threshold between 350-450 kPa was discovered that corresponded to both significant serosal thickness change and a positive histological trauma score rating. Whether the tissue injury quantified is pathologic is subject for future in-vivo longitudinal investigation but certainly based on literature, can be the basis of pathological adhesion formation or an area for hemorrhage and scar formation.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.332

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 integrity0.0000.001
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 head0.056
GPT teacher head0.348
Teacher spread0.292 · 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

Citations16
Published2019
Admission routes2
Has abstractyes

Explore more

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