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Record W4389286279 · doi:10.1101/2023.12.01.23299309

Acute intermittent hypoxia in people living with chronic stroke – a preliminary study to examine safety and efficacy as a neurorehabilitation intervention

2023· preprint· en· W4389286279 on OpenAlexaff
Gregory E. P. Pearcey, Alexander J. Barry, Milap S. Sandhu, Timothy J. Carroll, Elliot J. Roth, William Z. Rymer

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHigh Altitude and Hypoxia
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsNeurorehabilitationMedicineStroke (engine)Physical medicine and rehabilitationPhysical therapyAdverse effectIntervention (counseling)ElbowChronic strokeGrip strengthUpper limbHypoxia (environmental)RehabilitationInternal medicineSurgeryNursing

Abstract

fetched live from OpenAlex

ABSTRACT Background and Purpose Acute intermittent hypoxia (AIH) is a novel therapeutic intervention that has the potential to facilitate recovery of function, but its safety and efficacy have not been tested in people with stroke. The purpose here was to examine whether AIH is safe and effective in people with stroke. Methods Participants (n=10) with a unilateral, ischemic, hemispheric stroke were assessed before and following 4 sessions of AIH. Clinical tests and upper limb strength were assessed before, ∼15-30 minutes, and ∼60 minutes after the intervention. Results AIH was well-tolerated and there were no adverse events observed. Although no changes in strength were detected for the less-affected limb, grip strength and elbow flexion force of the more-affected limb was increased after AIH. Conclusions AIH appears to be potentially safe and effective for improving strength in the more-affected limb in people with stroke. Future work should explore the use of AIH to enhance task-specific training-induced plasticity.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.278
Teacher spread0.265 · 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 designNon-randomized 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

Citations1
Published2023
Admission routes1
Has abstractyes

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