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Record W3165815411 · doi:10.3822/ijtmb.v14i2.583

Acute and Chronic Periocular Massage for Ocular Blood Flow and Vision: a Randomized Controlled Trial

2021· article· en· W3165815411 on OpenAlexvenueno aff
Naoyuki Hayashi, MSc Lanfei Du

Bibliographic record

VenueInternational Journal of Therapeutic Massage & Bodywork Research Education & Practice · 2021
Typearticle
Languageen
FieldMedicine
TopicAcupuncture Treatment Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMassageMedicineVisual acuityOphthalmology

Abstract

fetched live from OpenAlex

INTRODUCTION: The ocular blood flow (OBF) is responsible for supplying nutrition to the retina, which plays a fundamental role in visual function. Massage is expected to improve the blood flow and, consequently, vascular function. The aim of this study was to determine the short-term and long-term effects of periocular massage on OBF and visual acuity. METHODS: The OBF and visual acuity were measured in 40 healthy adults aged 20-30 years before and after massage, and also in control subjects. Three massage methods were used: applying periocular acupressure ("Chinese eye exercise": CE), using a facial massage roller (MR), and using an automated eye massager (AM). The OBF and visual acuity were first measured before and after applying each type of massage for 5 min. Eye massage was then applied for 5 min once daily over a 60-day period, while the control group received no massage. The same measurements were then performed again. RESULTS: Performing short-term periocular massage showed significant interactions in time and massage effects on visual acuity in CE and AM groups, and on OBF in AM group, while 60-day massage period exerted no significant effects. No significant relationship was found between OBF and visual acuity changes. CONCLUSIONS: These results suggest that short-term periocular massage with Chinese eye exercise and automated eye massager can improve OBF and visual acuity, although no causal relationship was supported.

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)0.009
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.628
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.467
Teacher spread0.437 · 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 designRandomized trial
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

Citations10
Published2021
Admission routes1
Has abstractyes

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