Machine learning for predicting antimicrobial efficacy of periodontal gel formulations in vitro biofilm models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Periodontal disease caused by dysbiotic biofilms poses a major challenge and predicting the efficacy of topical antimicrobial gels is limited by biofilm resistance and resource-intensive in vitro testing. Therefore, it is of interest to develop machine learning (ML) models to predict antimicrobial efficacy of novel gel formulations against multi-species periodontal biofilms. Hence, a total of 120 formulations with varying polymers, agents, concentrations and enhancers were tested using the Calgary Biofilm Device and efficacy data were used to train Random Forest, SVM, Gradient Boosting and Neural Network models. Gradient Boosting achieved the best performance (accuracy 92.8%, AUC-ROC 0.96), with antimicrobial type, concentration and polymer viscosity as key predictors. ML, particularly Gradient Boosting, offers a reliable tool for predicting periodontal gel efficacy, enabling faster formulation optimization and reducing the need for extensive laboratory screening.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it