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Record W2598230006 · doi:10.1139/facets-2016-0006

Effect of water temperature on the hydrolysis of two absorbable sutures used in fish surgery

2016· article· en· W2598230006 on OpenAlexvenueno aff
Sarah A. Cannizzo, Simon C. Roe, Craig A. Harms, Michael K. Stoskopf

Bibliographic record

VenueFACETS · 2016
Typearticle
Languageen
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsnot available
FundersNorth Carolina State University
KeywordsUltimate tensile strengthFibrous jointAbsorbable sutureFish <Actinopterygii>Breaking strengthHydrolysisSurgeryMaterials scienceComposite materialChemistryMedicineBiologyFisheryBiochemistry

Abstract

fetched live from OpenAlex

Persistence of absorbable sutures in fishes in waters below 10 °C affects surgical decisions including approach, closure strategy, and suture selection. We hypothesized that the rate of suture hydrolysis would vary directly with water temperature. Two absorbable monofilament 3-0 suture materials used in fish surgery, poliglecaprone (Monocryl™) and polyglyconate (Maxon™), were evaluated. The maximum tensile load (strength) was measured for suture loops ( n = 6) maintained in filtered city water for 2, 4, 6, or 8 weeks at 4, 25, or 37 °C. For Maxon™ at 4 or 25 °C, tensile strength did not decrease over time. However, for Monocryl™ at 4 °C, 2-, 4-, and 8-week loops were stronger than baseline loops. At 25 °C, tensile strength of the suture material declined after 2 weeks. Also, at 37 °C, the optimal design temperature for both suture materials, the strength of Maxon™ decreased at 6 and 8 weeks. Two 4-week loops of Monocryl™ disintegrated when handled, and after 6 and 8 weeks, all were untestable. This study confirms that absorbable sutures lose strength more slowly at ambient temperatures lower than the optimal design temperature (e.g., human body temperature) and will likely be retained longer in fishes living in waters below 25 °C.

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 score0.121
Threshold uncertainty score0.624

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 head0.011
GPT teacher head0.256
Teacher spread0.245 · 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

Citations14
Published2016
Admission routes1
Has abstractyes

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