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Record W4381192770 · doi:10.1097/wnf.0000000000000557

Botulinum Toxins and Zinc: From Theory to Practice—A Systematic Review

2023· article· en· W4381192770 on OpenAlexaff
Farid Mallat, Jérôme Kaikati, Elio Kechichian

Bibliographic record

VenueClinical Neuropharmacology · 2023
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsBotulinum toxinBotulinum neurotoxinNeurotoxinMedicineLongevityClinical trialRandomized controlled trialMeta-analysisZincIntensive care medicineInternal medicineSurgeryToxinGerontologyBiologyChemistryMicrobiology

Abstract

fetched live from OpenAlex

OBJECTIVE: The aims of this study were to determine whether zinc supplementation affects botulinum toxin's effect and longevity and to establish a transition from the molecular to the clinical aspect. METHODS: We conducted a systematic review in which we included all published studies on PubMed and Embase using the combination of the following terms: "zinc" AND (botox OR botulinum OR onabotulinumtoxinA OR abobotulinumtoxinA OR incobotulinumtoxinA). RESULTS: From the 260 yielded articles, 3 randomized control trials and 1 case report were retained. Three of them found a significant improvement with zinc supplementation in the toxin's effect and longevity. This was observed in neurological conditions and cosmetic uses. CONCLUSIONS: Zinc supplementation could be an interesting asset in the potentialization of botulinum neurotoxin effect and longevity. Larger clinical trials and objective measurement tools should be used to further defining the role of zinc in maximizing botulinum neurotoxin effect.

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)0.015
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.064
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.006
Bibliometrics0.0180.015
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.044
GPT teacher head0.435
Teacher spread0.390 · 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 designSystematic review
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

Citations3
Published2023
Admission routes1
Has abstractyes

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