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Record W2312344257 · doi:10.1097/ico.0000000000000063

Comparison of Tensile Strength of Slip Knots With That of 3-1-1 Knots Using 10-0 Nylon Sutures

2014· article· en· W2312344257 on OpenAlexafffund
Carla Lutchman, Linus H. Leung, Rahim Moineddin, Hall F. Chew

Bibliographic record

VenueCornea · 2014
Typearticle
Languageen
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersUniversity of Toronto
KeywordsUltimate tensile strengthKnot (papermaking)Slip (aerodynamics)Materials scienceComposite materialPhysicsThermodynamics

Abstract

fetched live from OpenAlex

PURPOSE: The aim of this study was to compare the tensile strength of slip knots with that of 3-1-1 knots using 10-0 nylon sutures. METHODS: In vitro, destructive materials testing was used. By adhering to the American Standard for Testing and Materials standards for testing of suture materials, slip knots were compared with 3-1-1 knots using 10-0 nylon suture material. Tensile testing was performed on each knot type using the Instron Microtester (Model 5848 Norwood, MA). Scanning electron microscopy was used to analyze all sutures tested to failure. The main outcome measure was the maximum load (newtons) or ultimate tensile strength before which each knot failed by breakage or by unraveling. RESULTS: The mean force resulting in failure by breakage of the 3-1-1 knot and slip knot was 0.71 and 0.64 N, respectively (P = 0.048). The mean force resulting in failure by the unraveling of the 3-1-1 knot and slip knot was 0.48 and 0.37 N, respectively (P = 0.022). CONCLUSIONS: In 10-0 nylon sutures, the 3-1-1 knot has a statistically significant greater tensile strength than the slip knot has in conditions wherein they fail by either breakage or unraveling.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.060
GPT teacher head0.330
Teacher spread0.270 · 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

Citations7
Published2014
Admission routes2
Has abstractyes

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