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Record W2048842194 · doi:10.3390/s130708523

Major Odorants Released as Urinary Volatiles by Urinary Incontinent Patients

2013· article· en· W2048842194 on OpenAlexaff
Sudhir Kumar Pandey, Ki‐Hyun Kim, Si Kyung Choi, In Sa, Soo Yeon Oh

Bibliographic record

VenueSensors · 2013
Typearticle
Languageen
FieldMedicine
TopicNasal Surgery and Airway Studies
Canadian institutionsKimberly-Clark (Canada)
FundersNational Research Foundation of KoreaNational Research FoundationMinistry of Education, Science and TechnologyKimberly-Clark
KeywordsOdorMethanethiolTrimethylamineUrineChemistryChromatographyUrinary systemHydrogen sulfideUrine sampleHexanalAcetaldehydeDimethyl sulfideUrinary incontinenceSulfurOrganic chemistryMedicineBiochemistryUrologyInternal medicine

Abstract

fetched live from OpenAlex

In this study, volatile urinary components were collected using three different types of samples from patients suffering from urinary incontinence (UI): (1) urine (A); (2) urine + non-used pad (B); and (3) urine + used pad (C). In addition, urine + non-used pad (D) samples from non-patients were also collected as a reference. The collection of urinary volatiles was conducted with the aid of a glass impinger-based mini-chamber method. Each of the four sample types (A through D) was placed in a glass impinger and incubated for 4 hours at 37 °C. Ultra pure air was then passed through the chamber, and volatile urine gas components were collected into Tedlar bags at the other end. These bag samples were then analyzed for a wide range of VOCs and major offensive odorants (e.g., reduced sulfur compounds (RSCs), carbonyls, trimethylamine (TMA), ammonia, etc.). Among the various odorants, sulfur compounds (methanethiol and hydrogen sulfide) and aldehydes (acetaldehyde, butylaldehyde, and isovaleraldehyde) were detected above odor threshold and predicted to contribute most effectively to odor intensity of urine incontinence.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score1.000

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)0.0010.001

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 head0.008
GPT teacher head0.229
Teacher spread0.221 · 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.

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

Citations9
Published2013
Admission routes1
Has abstractyes

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