MétaCan
Menu
Back to cohort
Record W2209218544 · doi:10.1260/2040-2295.6.4.673

Predicting Neck Fluid Accumulation While Supine

2015· article· en· W2209218544 on OpenAlexaff
Daniel Vena, Babak Taati, Azadeh Yadollahi

Bibliographic record

VenueJournal of Healthcare Engineering · 2015
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsSupine positionBioelectrical impedance analysisMedicineAnthropometryBody fluidDemographicsInternal medicineBody mass index

Abstract

fetched live from OpenAlex

When lying supine, fluid shifts rostrally from the legs and accumulates in the neck, which is a risk factor for obstructive sleep apnea. The objective of this study was to model neck fluid accumulation using one-time baseline measurements of body fluid, demographics, and anthropometrics. Using bioelectrical impedance, leg and neck fluid volumes (LFV and NFV) were measured continuously and simultaneously. Thirty non-obese adults (13 men) stood quietly for 5 minutes, and then lay supine for 90 minutes while fluid volumes were measured. Neck circumference (NC) was measured before and after the supine period. Results demonstrated that, compared to women, men experienced a greater increase in NC after lying supine. Furthermore, baseline LFV at the onset of lying supine was significantly correlated with ΔLFV (r = 0.44, p = 0.014) and ΔNC (r = 0.51, p = 0.008) after 90 minutes supine. The findings identify that sex and baseline LFV predict both the fluid leaving the legs and increase in NC during recumbency.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.077
GPT teacher head0.359
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Healthcare EngineeringSame topicObstructive Sleep Apnea ResearchFrench-language works237,207