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Postural Orthostatic Tachycardia Syndrome: Mechanisms and New Therapies

2019· review· en· W2967501582 on OpenAlexafffund
Philip L. Mar, Satish R. Raj

Bibliographic record

VenueAnnual Review of Medicine · 2019
Typereview
Languageen
FieldMedicine
TopicCardiovascular Syncope and Autonomic Disorders
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
FundersNational Center for Advancing Translational SciencesCanadian Institutes of Health Research
KeywordsMedicineOrthostatic vital signsOrthostatic intolerancePathophysiologyPostural Orthostatic Tachycardia SyndromeTachycardiaHypovolemiaAnesthesiaCardiologyInternal medicineBlood pressure

Abstract

fetched live from OpenAlex

Postural orthostatic tachycardia syndrome (POTS) is a clinically heterogeneous disorder with multiple contributing pathophysiologic mechanisms manifesting as symptoms of orthostatic intolerance in the setting of orthostatic tachycardia (increase in heart rate by at least 30 beats per minute upon assuming an upright position) without orthostatic hypotension. The three major pathophysiologic mechanisms include partial autonomic neuropathy, hypovolemia, and hyperadrenergic state. Patients often will exhibit overlapping characteristics from more than one of these mechanisms. The approach to the treatment of POTS centers on treating the underlying pathophysiologic mechanism. Stockings, abdominal binders, and vasoconstrictors are used to enhance venous return in partial neuropathic POTS. Exercise and volume expansion are the main treatment strategies for hypo-volemic POTS. For hyperadrenergic POTS, beta-blockers and avoidance of norepinephrine reuptake inhibitors is important. Attempts should be made to discern which pathophysiologic mechanism(s) may be afflicting patients so that treatment regimens can be individualized.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.033
GPT teacher head0.338
Teacher spread0.305 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations112
Published2019
Admission routes2
Has abstractyes

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