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Record W3015071616 · doi:10.17126/joralres.2019.071

Efficacy and safety of beta carotones in treatment of oral leukoplakia: systematic review and meta-analysis.

2020· article· en· W3015071616 on OpenAlexaff
Rania Shalaby, Yehia Fathi, Basma Elsaadany

Bibliographic record

VenueJournal Of Oral Research · 2020
Typearticle
Languageen
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMeta-analysisMedicineOral leukoplakiaLeukoplakiaDentistryInternal medicineCancer

Abstract

fetched live from OpenAlex

Objectives: A systematic review was conducted to evaluate effectiveness and safety of beta carotenes for the treatment of oral leukoplakia regarding clinical resolution and prevention of malignant transformation. Material and Methods: The systematic search was conducted in three electronic databases and the study’s selection was performed according to pre-set eligibility criteria. Four studies evaluating the efficacy of beta carotenes in oral leukoplakia compared to placebo were included in the review; three of which were assigned for quantitative analysis. Data were extracted, tabulated, quality assessed and statistically analyzed. Results: The meta-analysis revealed that when comparing clinical resolution the beta carotene group favored was favored compared to placebo, with statistically significant difference. However, a meta-analysis comparing beta carotene and placebo groups regarding malignant transformation as a primary outcome failed to show any significant benefit. Furthermore, results showed evidence of beta carotene safety. Conclusion: the overall quality of evidence about efficacy of beta carotene in oral leukoplakia treatment was not high. However, given the obvious safety of this agent, data suggests it could have a promising effect in clinical improvement of oral leukoplakia lesions. However, no evidence supporting its benefits in reducing risk of malignant transformation in these lesions was found. Therefore, further long term, well designed randomized clinical trials are highly recommended.

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)0.002
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: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
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 head0.365
GPT teacher head0.509
Teacher spread0.144 · 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 designMeta-analysis
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

Citations0
Published2020
Admission routes1
Has abstractyes

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