ArticleScientific reports2025
Retrospective analysis of dental chair failures and usage patterns in academic dental clinics.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dental chairs are essential for sustaining clinical care, yet their reliability in academic environments has rarely been quantified. This retrospective study, conducted across multiple clinics at Al-Andalus University for Medical Sciences (Tartous, Syria), integrated weekly usage logs, coded failure records, and equipment lifespan data to examine the relationship between workload and malfunction. Analysis revealed that prosthodontic and restorative clinics experienced the highest workloads and the greatest number of failures. Saliva ejectors, delivery systems, and control units accounted for most recurrent incidents, with a small subset of fault types responsible for the majority of breakdowns. The relationship between chair lifespan and fault frequency was generally weak, suggesting that age alone poorly predicts malfunction. Predictive modeling with random forest and linear regression demonstrated limited accuracy (R² ≈ 0.15) but revealed that case mix was a stronger driver of workload variation than calendar-related factors. Overall, the findings indicate that failure risk is shaped primarily by clinical specialization and workload intensity. Adapting maintenance strategies to actual usage and case profiles-rather than applying uniform schedules-may reduce downtime and improve patient flow. Expanding datasets and integrating real-time operational data could further strengthen predictive maintenance in academic dental settings.
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