患者报告的结果测量 (PROM) 和临床医生报告的结果 (ClinRO) 在鼻地板升高用于植入物康复在牙无大部:系统性审查和COSMIN分析
Hamoun Sabri1,2, Muhammad H A Saleh1, Jacob Martin Zimmer1
1Department of Periodontics and Oral Medicine, University of Michigan School of Dentistry, Ann Arbor, Michigan, USA.
Clinical oral implants research
|February 24, 2026
概括
这项研究回顾了牙科植入物中鼻腔底部升高 (SFE) 后的结果. 临床人员的结果集中在植入物上,而患者的结果需要更好的验证,建议OHIP-49和DSQ.
科学领域:
- 口腔和牙面部外科手术
- 牙周病学 牙周病学
- 牙科植入物学 牙科植入物学
背景情况:
- 阴道地板高度 (SFE) 对于在无牙大部的植入物康复至关重要.
- 在SFE程序中缺乏标准化的结果评估.
研究的目的:
- 系统地评估患者报告的结果措施 (PROMs) 和临床医生报告的结果 (ClinROs) 在SFE之后.
- 评估SFE研究中使用的PROM的质量.
主要方法:
- 从2013年到2024年进行系统的文献搜索 (PubMed,EMBASE,Scopus,Cochrane)
- 包括评估PROM和ClinRO的前性研究.
- 使用COSMIN检查清单进行PROM质量评估.
主要成果:
- 包括18项研究;14项ClinRO专注于植入物存活率和骨水平.
- 特定于SFE的结局 (骨变化,穿孔) 和假肢结局报告不足.
- 50%的研究报告了PROM,其中OHIP-49和DSQ显示出强烈的心理测量特性.
结论:
- 在ClinRO报告中,植入物指标优先于SFE特定数据.
- 经过验证的PROM对于一致可靠的结果评估至关重要.
- 建议OHIP-49和DSQ是核心PROM;未来的研究需要标准化的报告和长期PROM的整合.
更多相关视频
05:54Real-Time Dynamic Navigation System for the Precise Quad-Zygomatic Implant Placement in a Patient with a Severely Atrophic Maxilla
Published on: October 18, 2021
2.3K
07:32Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment
Published on: February 23, 2024
2.0K
相关概念视频
01:26Methods of Documentation II: POMR
1.4K
The Problem-Oriented Medical Record (POMR) revolutionized medical record-keeping by introducing a systematic approach focusing on the patient's problems rather than merely listing symptoms. Dr. Lawrence Weed's introduction of this method in the 1960s marked a significant advancement in medical documentation. The POMR framework consists of four key components: the database, problem list, plan of care, and progress notes.
1.4K
01:15Receiver Operating Characteristic Plot
584
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
584