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Record W2090176622 · doi:10.1089/end.2010.0722

Drainage Characteristics of the 3F MicroStent Using a Novel Film Occlusion Anchoring Mechanism

2011· article· en· W2090176622 on OpenAlexaff
Dirk Lange, Nathan Hoag, Beow Kiong Poh, Ben H. Chew

Bibliographic record

VenueJournal of Endourology · 2011
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsUreterStentEx vivoMedicineSiliconeOcclusionIn vivoUrologySurgeryChemistryBiology

Abstract

fetched live from OpenAlex

PURPOSE: To determine whether the overall ureteral flow through an obstructed ureter using the 3F MicroStent™ that uses a novel film occlusion anchoring mechanism is comparable to the flow using a conventional 3F and 4.7F Double-J stent. MATERIALS AND METHODS: An in vitro silicone ureter model and an ex vivo porcine urinary model (kidney and ureter) were used to measure the overall flow through obstructed and unobstructed ureters with either a 3F Double-J stent (Cook), 3F MicroStent (PercSys), or 4.7F Double-J stent (Cook). Mean flow rates were compared with descriptive statistics. RESULTS: Mean flow rates through the obstructed silicone ureter (12-mm stone) for the 3F MicroStent, 3F Double-J stent, and 4.7F Double-J stent were 326.7±13.3 mL/min, 283.3±19.2 mL/min, and 356.7±14.1 mL/min, respectively. In the obstructed ex vivo porcine ureter model, the flow as a percentage of free flow was 60%, 53%, and 50 %, respectively. In both ureteral models, flow rates of the 3F MicroStent and 4.7F Double-J stents were not statistically different. CONCLUSIONS: The 3F MicroStent demonstrated drainage equivalent to a 4.7F Double-J stent, in both in vitro silicone and ex vivo porcine obstructed urinary models. We have demonstrated the crucial first step that this 3F stent, using a novel film occlusion anchoring mechanism, has equivalent, if not slightly improved, drainage rates when compared with its larger counterpart.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.802
Threshold uncertainty score0.288

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 head0.048
GPT teacher head0.271
Teacher spread0.223 · 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

Citations13
Published2011
Admission routes1
Has abstractyes

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