Search strategies on the impact of using SDF 38% on the development of dental caries and root caries in older adults / Stratégies de recherche sur l’impact de l’utilisation de SDF 38 % sur le développement des caries dentaires et des caries radiculaires chez les personnes âgées
Bibliographic record
Abstract
Ce jeu de données contient les stratégies de recherche pour Medline All (Ovid), All EBM Reviews (Ovid) et Web of Science Core Collection (AHCI, ESCI, SCIe, SSCI). Lorsqu’elles sont exécutées dans leurs bases de données respectives, ces stratégies permettent de repérer des articles portant sur les concepts de fluorure de diamine d’argent (FDA), de carie dentaire, de caries radiculaires et de personnes âgées. Le nombre de résultats et les stratégies de recherche pour chaque base de données sont indiqués dans le fichier rtf. Les recherches ont été effectuées le 9 mai 2025 et mises à jour le 16 mai 2025. / This dataset contains search strategies for MEDLINE All (Ovid), All EBM Reviews (Ovid) and Web of Science Core Collection (AHCI, ESCI, SCIe, SSCI). When run in their respective databases, these strategies retrieve articles on the concepts of silver diamine fluoride, dental caries, root caries, and older adults. The number of results and search strategies for each database are indicated in the rtf file. The searches were conducted on May 09, 2025 and updated on May 16, 2025.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.044 | 0.034 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 0.007 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".