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Record W2056297429 · doi:10.3791/4315

Tilt Testing with Combined Lower Body Negative Pressure: a "Gold Standard" for Measuring Orthostatic Tolerance

2013· article· en· W2056297429 on OpenAlexaff
Clare Louise Protheroe, Henrike Joanna Cornelie Ravensbergen, Jessica A. Inskip, Victoria E. Claydon

Bibliographic record

VenueJournal of Visualized Experiments · 2013
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Syncope and Autonomic Disorders
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGold standard (test)Orthostatic vital signsLower bodyTilt (camera)MedicinePhysical medicine and rehabilitationBlood pressureInternal medicineMathematicsGeometry

Abstract

fetched live from OpenAlex

Orthostatic tolerance (OT) refers to the ability to maintain cardiovascular stability when upright, against the hydrostatic effects of gravity, and hence to maintain cerebral perfusion and prevent syncope (fainting). Various techniques are available to assess OT and the effects of gravitational stress upon the circulation, typically by reproducing a presyncopal event (near-fainting episode) in a controlled laboratory environment. The time and/or degree of stress required to provoke this response provides the measure of OT. Any technique used to determine OT should: enable distinction between patients with orthostatic intolerance (of various causes) and asymptomatic control subjects; be highly reproducible, enabling evaluation of therapeutic interventions; avoid invasive procedures, which are known to impair OT(1). In the late 1980s head-upright tilt testing was first utilized for diagnosing syncope(2). Since then it has been used to assess OT in patients with syncope of unknown cause, as well as in healthy subjects to study postural cardiovascular reflexes(2-6). Tilting protocols comprise three categories: passive tilt; passive tilt accompanied by pharmacological provocation; and passive tilt with combined lower body negative pressure (LBNP). However, the effects of tilt testing (and other orthostatic stress testing modalities) are often poorly reproducible, with low sensitivity and specificity to diagnose orthostatic intolerance(7). Typically, a passive tilt includes 20-60 min of orthostatic stress continued until the onset of presyncope in patients(2-6). However, the main drawback of this procedure is its inability to invoke presyncope in all individuals undergoing the test, and corresponding low sensitivity(8,9). Thus, different methods were explored to increase the orthostatic stress and improve sensitivity. Pharmacological provocation has been used to increase the orthostatic challenge, for example using isoprenaline(4,7,10,11) or sublingual nitrate(12,13). However, the main drawback of these approaches are increases in sensitivity at the cost of unacceptable decreases in specificity(10,14), with a high positive response rate immediately after administration(15). Furthermore, invasive procedures associated with some pharmacological provocations greatly increase the false positive rate(1). Another approach is to combine passive tilt testing with LBNP, providing a stronger orthostatic stress without invasive procedures or drug side-effects, using the technique pioneered by Professor Roger Hainsworth in the 1990s(16-18). This approach provokes presyncope in almost all subjects (allowing for symptom recognition in patients with syncope), while discriminating between patients with syncope and healthy controls, with a specificity of 92%, sensitivity of 85%, and repeatability of 1.1±0.6 min(16,17). This allows not only diagnosis and pathophysiological assessment(19-22), but also the evaluation of treatments for orthostatic intolerance due to its high repeatability(23-30). For these reasons, we argue this should be the "gold standard" for orthostatic stress testing, and accordingly this will be the method described in this paper.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.029
GPT teacher head0.355
Teacher spread0.325 · 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 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

Citations31
Published2013
Admission routes1
Has abstractyes

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