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Record W4366442939 · doi:10.2196/42787

Performance and Safety of a New Medical Device (Polybactum) for Reducing the Recurrence Rate of Bacterial Vaginosis: Protocol for a Multicenter, Open-Label, Noncontrolled International Clinical Trial (POLARIS Study)

2023· article· en· W4366442939 on OpenAlexvenueno aff
Filippo Murina, Paolo Inghirami, Marius Biriş, Daniela Sîrbu, Dionisio Franco Barattini, Federica Sbrocca, Luca Ivan Ardolino, Mario Mangrella, Elena Casolati, Serban Roșu, Ciprian Crișan

Bibliographic record

VenueJMIR Research Protocols · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicReproductive tract infections research
Canadian institutionsnot available
Fundersnot available
KeywordsBacterial vaginosisMedicineGardnerella vaginalisInformed consentClinical trialInternal medicineAttritionRandomizationPediatricsSurgeryGynecologyAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The medical literature has reported that recurrent bacterial vaginosis (RBV) has a relapse rate of 35% within 3 months and 60% within 12 months after antibiotic administration. Products that are able to provide a barrier effect against the biofilm produced by Gardnerella vaginalis could play a role in improving the results of bacterial vaginosis (BV) treatment. OBJECTIVE: This study aims to assess the performance and safety of a medical device (Polybactum) containing polycarbophil, lauryl glucoside, and glycerides (PLGG) for reducing the rate of recurrence of BV. METHODS: The study includes women who are aged above 18 years, are willing to provide signed informed consent, have a diagnosis of BV according to the Amsel criteria, and have been affected by at least 2 episodes of RBV in the last 12 months. The trial includes 2 phases. In the first phase (according to an open-label noncontrolled design), the treatment involving PLGG is administered for 3 cycles and is followed by a 1-month period of follow-up without treatment. In the second phase, a 9-month follow-up period is envisaged. Thus, for each patient, a 10-month follow-up period without treatment is planned. The study involves 5 centers (2 in Italy and 3 in Romania). We calculated a sample size of 44 pairs to achieve a power of 80% and a 1-sided significance of 5% for detecting a difference of 0.25 between marginal proportions, in comparison with the mean recurrence rate of BV reported in the medical literature. We estimated a potential dropout rate of 20%, and thus, we decided to enroll 55 patients (1-group chi-square test). RESULTS: The study received ethics approval in 2016 in Romania and 1 year later in Italy. Recruitment started in September 2016. An interim analysis was performed in 2019, and full study analysis results are expected in July 2023. CONCLUSIONS: The tested medical device involving PLGG could modify the mechanisms involved in the pathogenesis of BV and could improve microbiological parameters owing to the acidifying effect on vaginal pH. We believe that the findings of our study could be useful for other investigators who want to test different products against RBV using a standardized protocol and standardized procedures. TRIAL REGISTRATION: ClinicalTrials.gov NCT02863536; https://clinicaltrials.gov/ct2/show/NCT02863536. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/42787.

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)0.013
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.020
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.009
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0200.004

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 head0.481
GPT teacher head0.627
Teacher spread0.146 · 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 designNon-randomized trial
Domainnot available
GenreProtocol

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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