Bibliographic record
Abstract
Adverse drug reactions (ADRs) are one of the major culprits in the development of oral lesions, which can be misdiagnosed with underlying diseases. The goal of this study is to summarize and update the current knowledge about drug-induced oral reactions. Electronic searches were performed in Scopus, Google Scholar, Cochrane and PubMed databases, for articles published between January 2008 and August 2017. Two authors screened the titles and abstracts for eligibility. Finally, 56 studies included in this review. There was no systematic homogeneity in the included studies; thereby no meta-analysis was performed. The most frequent oral ADR was xerostomia,andthe most reported cause was antihypertensive medications. Cardiovascular drugs were the most reported culprit agents for induction of oral ulcerative and vesiculo-bullous lesions, followed by methotrexate. Nonsteroidal anti-inflammatory drugs (NSAIDs) and β-blockers were found the most common responsible drugs for induction of oral lichen planus. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".