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Medicinal Plants Used to Treat Oral Diseases in the Lepelle-Nkumpi Municipality, Limpopo Province, South Africa

2019· article· en· W4213022245 on OpenAlexvenueno aff
Sebua Silas Semenya, Sekgothe Mokgoatšana, Alfred Maroyi

Bibliographic record

VenueJournal of Pharmacy and Nutrition Sciences · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEthnobotanical and Medicinal Plants Studies
Canadian institutionsnot available
FundersUniversity of Fort HareNational Research Foundation
KeywordsTraditional medicineToothacheMedicineMedicinal plantsPlant speciesOral healthEthnobotanyDentistryBiologyBotany

Abstract

fetched live from OpenAlex

Purpose: Therefore, this study was aimed at documenting medicinal plants used to treat oral diseases in the Lepelle-Nkumpi Municipality in the Limpopo Province, South Africa.Methods: Data was collected using a semi-structured questionnaire, supplemented by field observations with 30 traditional healers from the Lepelle-Nkumpi Municipality in the Capricorn District of the Limpopo Province in South Africa.Results: A total of 41 plant species belonging to 30 botanical families, mainly the Asteraceae and Solanaceae (13.3% each), were reported as remedies for different oral diseases. Leaves (51%) and roots (28%), harvested from herbs and trees were preferred for medicinal preparations. The majority of plant species (60.4%) were used as monotherapy, to treat a single oral disease, while the remainder (39.6%) treated more than one ailment. The majority of the species (44.2%) were used as herbal medicines for toothache, followed by 32.6% used against bad breath and 25.6% used against dental caries.Conclusion: Traditional healers play an important role in the provision of primary health care as oral pathogens are also responsible for nonstomatological infections.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.167

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.119
GPT teacher head0.344
Teacher spread0.225 · 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 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

Citations3
Published2019
Admission routes1
Has abstractyes

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