Chemotherapy with Alkylating Agents and Dental Anomalies in Children: A Systematic Review
Bibliographic record
Abstract
The aim of the systematic review is to analyze the type and the prevalence of dental side effects among cancer survivors treated with alkylating agents (AAs) during pediatric age. Moreover, the study aimed to investigate the association between the development of dental anomalies and the drug used or the tumor type. Four databases MEDLINE-PubMed, Web of Science, Scopus, and the Cochrane Central Register of Controlled Trials (CENTRAL) were searched from January 2024 to March 2024. All articles published up to March 2024 were evaluated. After removing duplicates, data extraction and risk of bias assessment using the Newcastle-Ottawa score were made. A summary of the overall strength of evidence available was performed using the "Grading of Recommendations Assessment, Development and Evaluation" (GRADE). Data were summarized using descriptive analysis as mean differences ± standard deviation or relative risks. Out of 2678 studies, the search identified five studies enrolled for the qualitative analysis of the data. Among 257 survivors, 155 (60.3%) reported: microdontia, agenesia, root shortening, enamel defects, and taurodontism. Microdontia occurred more frequently with other drugs compared to AAs. In conclusion, children treated with AAs showed microdontia (36.0%), root shortening (26.9%), and agenesis (23.5%). Secondly, the occurrence of dental anomalies was unaffected by drug treatment; thirdly, microdontia was the most frequent dental anomaly observed in both solid and lymphoproliferative tumors. This review was performed in accordance with the PRISMA guidelines. PROSPERO registration number CRD42023494560.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".