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Record W3109059747 · doi:10.32778/spvs.71366.2020.12

Clinical application of visceral thoracic osteopathic manipulations in heart rate variability measurement with photoplethysmography for stress-related symptoms: a case report

2020· article· en· W3109059747 on OpenAlexaff
Julien Fatisson, François Lalonde, Victor Oswald, Yannick Mullié

Bibliographic record

VenueScholar Pilot and Validation Studies · 2020
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversité de MontréalUniversité du Québec à MontréalCollège d'Études Ostéopathiques de Montréal
Fundersnot available
KeywordsHeart rate variabilityHeartbeatMedicineOsteopathyHeart rateCardiologyInternal medicinePathologyComputer scienceAlternative medicineBlood pressure

Abstract

fetched live from OpenAlex

Abstract Heart rate variability (HRV) is the change in frequency from one heartbeat to the next, due to the adaptation of the heart’s rhythm in response to an internal or external stimulus. Increased HRV has been reported as a potential indicator of general health. The relationship between structure and function is one of the guiding principles of osteopathy; it considers anatomy essential for explaining physiological dysfunction. There is a paucity of research on the effects of Osteopathic manipulative medicine (OMM) on the effects of HRV. We hypothesize that OMM can be used safely and to help promote healthier HRV. We herein report on a case of improved HRV after serial OMM sessions.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.001
Science and technology studies0.0020.002
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0020.001

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 head0.139
GPT teacher head0.396
Teacher spread0.258 · 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 designCase report
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

Citations2
Published2020
Admission routes1
Has abstractyes

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