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Record W4362620088 · doi:10.4000/vertigo.39346

Usage thérapeutique du manguier (Mangifera indica L., Anacardiaceae) au Burkina Faso

2023· article· fr· W4362620088 on OpenAlexvenueno aff
Corneille Drabo, Zara Soutonnoma Nikiema, Oumarou Zoéyandé Dianda, Abdalla Dao, Jacob Sanou, Mahamadou Sawadogo

Bibliographic record

VenueVertigO · 2023
Typearticle
Languagefr
FieldMedicine
TopicMangiferin and Mango Extracts
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesForestryArtGeography

Abstract

fetched live from OpenAlex

Les extraits d’organes de manguier (Mangifera indica L., Anacardiacaea) présentent un potentiel d’utilisation dans des applications pharmaceutiques. Le but de cette étude est de contribuer à une meilleure connaissance des vertus thérapeutiques du manguier au Burkina Faso. Des enquêtes ethnobotaniques ont été réalisées à l’aide de questionnaire auprès des phytothérapeutes de trois régions du Burkina Faso (Centre-Ouest, Hauts-Bassins et Cascades). Les résultats ont été obtenus auprès de 45 phytothérapeutes enquêtés. Ces phytothérapeutes sont représentés par 79% d’hommes et 21% de femmes. La tranche d’âge comprise entre 31 et 40 ans est la plus représentée, soit 32,41% et 59,25% des phytothérapeutes enquêté sont non scolarisés. Les résultats obtenus ont permis de répertorier des maladies couramment traitées avec les organes du manguier. De plus, les feuilles du manguier des variétés ordinaires (Mangot vert et sabre) surtout sont les plus utilisées dans la préparation médicamenteuse. La décoction est le mode de préparation le plus sollicité et l’administration des remèdes se fait par voie orale (100%). Une étude pharmacologique devrait permettre l’incorporation des extraits des organes du manguier dans des compléments alimentaires pour traiter certaines maladies.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.272
Teacher spread0.255 · 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 designObservational
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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Same venueVertigOSame topicMangiferin and Mango ExtractsFrench-language works237,207