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Record W2971601069 · doi:10.18192/osurj.v2i1.4173

Medicinal Marijuana: A Promising Potential Treatment for Obstructive Sleep Apnea

2019· article· en· W2971601069 on OpenAlexaffvenue
Gurvir Rai, Anokhee Thakker, Hasti Patel

Bibliographic record

VenueUniversity of Ottawa Science Undergraduate Research Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsObstructive sleep apneaMedicineSleep (system call)Sleep apneaDiseaseApneaSleep disorderContinuous positive airway pressureIntensive care medicineInsomniaAnesthesiaInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Obstructive Sleep Apnea (OSA) is a common sleep disorder in mammals which is characterized by the repeated collapse of the upper airway. It is a known cause of hypertension, cardiovascular disease, and, in some cases, death. OSA is commonly treated by medical devices and surgeries. Although effective, these treatments are associated with unwanted side-effects and further complications. In recent years, the role of medicinal marijuana has grown as a potential treatment for sleep disorders. Further research must be conducted to determine the efficacy and safety of cannabis as a treatment for OSA.

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)0.000
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: Other · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.030
GPT teacher head0.326
Teacher spread0.296 · 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
GenreOther

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

Citations0
Published2019
Admission routes2
Has abstractyes

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