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Record W2002408676 · doi:10.1111/papr.12284

Traditional Uses of Medicinal Plants from the Canadian Boreal Forest for the Management of Chronic Pain Syndromes

2015· review· en· W2002408676 on OpenAlexaffabout
Yadav Uprety, Anaïs Lacasse, Hugo Asselin

Bibliographic record

VenuePain Practice · 2015
Typereview
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsMedicineChronic painRheumatismMedicinal plantsAnalgesicAllopathic medicineMigraineChronic MigraineTraditional medicineAlternative medicinePharmacologyPhysical therapyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Chronic pain is more prevalent in indigenous populations who often prefer traditional remedies over allopathic drugs. Our objective was to investigate the traditional uses of medicinal plants from the Canadian boreal forest for the management of chronic pain syndromes. METHODS: We reviewed the most extensive database on medicinal plants used by aboriginal people of the Canadian boreal forest to investigate the plants used in the management of 3 of the most common chronic pain syndromes: arthritis/rheumatism; back pain; and headache/migraine. We also reviewed the pharmacology and phytochemistry literature to investigate concordance with indigenous knowledge. RESULTS: A total of 114 medicinal plant species were reported, of which 27 (23.5%) were used to treat more than 1 chronic pain syndrome. Pharmacological or phytochemical evidence to explain plant function as chronic pain remedy was available in the literature for only 38 species (33%), with several species reported to have anti-inflammatory and analgesic properties effective in treating chronic pain syndromes. CONCLUSIONS: Our study showed the potential of boreal plants as alternative and complementary medicines for the treatment of chronic pain syndromes that could be enhanced by further research on efficacy and safety issues.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.966
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.156
GPT teacher head0.371
Teacher spread0.215 · 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.

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

Citations14
Published2015
Admission routes2
Has abstractyes

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