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Journal of Peking University (Health Sciences) ›› 2023, Vol. 55 ›› Issue (1): 108-113. doi: 10.19723/j.issn.1671-167X.2023.01.016

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Prediction, analysis and application of learning curve of tooth preparation for all ceramic crowns of maxillary central incisors

Si-yu WU,Ya-ning LI,Xiao ZHANG,Long-wei LV,Yun-song LIU,Hong-qiang YE*(),Yong-sheng ZHOU*()   

  1. Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China
  • Received:2022-10-06 Online:2023-02-18 Published:2023-01-31
  • Contact: Hong-qiang YE,Yong-sheng ZHOU E-mail:yehongqiang@hsc.pku.edu.cn;kqzhouysh@hsc.pku.edu.cn
  • Supported by:
    the Teaching Reformation Fund of Peking University School and Hospital of Stomatology(2022-ZD-01);Peking University Health Science Center Medical Education Research Funding Project(2022-ZD-05);the Quality Improvement Project of Standardized Training of Residents of Beijing(2022-TGXM-02)

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Abstract:

Objective: To predict the learning curve of tooth preparation for all ceramic crowns of maxillary central incisors on phantom head simulators for graduate students participating in standardized dental resident training based on the modified Wright learning curve model, then to analyze and applicate the learning curve. Methods: Twelve graduate students participating in standardized dental resident training were selected to prepare the resin maxillary central incisors on phantom head simulators for all ceramic crowns 4 times. The results of preparation were evaluated by 3 prosthetic experts with at least 10 years' experience focusing on the reduction, contour, taper, shoulder, finish line, margin placement, adjacent tooth injury, and preparation time for tooth preparation. The learning rate of tooth preparation was calculated by scores of tooth preparation of 4 times. The learning curve of tooth preparation was predicted based on the modified Wright learning curve model. According to the criteria of standardized training skill examinations for dental residents in Beijing, 80 was taken as the qualified standard score. The minimum training times for tooth preparation to satisfy the qualified standard score (80) was calculated, to analyze the characteristics of learning curve and evaluate the effectiveness of tooth preparation. Results: The scores of 4 tooth preparation were 64.03±7.80, 71.40±6.13, 74.33±5.96, and 75.98±4.52, respectively. The learning rate was (106±4)%, which showed the learning curve an upward trend. There were no significant differences between the qualified standard score and the predicted scores of tooth preparation from the 5th preparation to the 13th preparation (P > 0.05). The predicted score of the 14th preparation was higher than the qualified standard score (P < 0.05). Conclusion: The trend of the learning curve of tooth preparation for all ceramic crowns of maxillary central incisors on phantom head simulators for graduate students participating in standardized dental resident training is upward, which predicts the minimum training times higher than the qualified standard score is 14 times.

Key words: Dental porcelain, Tooth preparation, Learning curve, Prosthodontic

CLC Number: 

  • R783.2

Table 1

Criteria for evaluation of tooth preparation for all ceramic crown of maxillary central incisor"

Feature Criteria
Reduction (20 points)
  Incisal reduction (5 points) 1.4-1.6 mm: 5 points
1.2-1.4 mm or 1.6-1.8 mm: 3 points
1.0-1.2 mm or 1.8-2.0 mm: 1 point
< 1.0 mm or > 2.0 mm: 0 point
  Labial reduction (5 points)
  Proximal reduction (5 points)
  Lingual reduction (5 points)		 1.0-1.5 mm: 5 points
0.8-1.0 mm or 1.5-1.7 mm: 3 points
0.5-0.8 mm or 1.7-2.0 mm: 1 point
< 0.5 mm or > 2.0 mm: 0 point
Contour (15 points)
  Labial contour (5 points) Labial preparation has two planes providing adequate material bulk for strength/esthetics
Cervical 1/2 surface parallels with the path of insertion
  Proximal contour (5 points) Smooth connected with labial and lingual surfaces, without undercut
  Lingual contour (5 points) Coincided with lingual fossa, without undercut
Taper (20 points)
  Labial-lingual taper (10 points) 0°-10°: 10 points
10°-20°: 8 points
  Mesial-distal taper (10 points) 20°-30°: 5 points
< 0° or > 30°: 0 point
Shoulder (10 points)
  (5 points) 1.0 mm shoulder
  (5 points) Obtuse inner line angles, continuous without sharp edges
Finish line (10 points) Smooth, continuous, well-defined: 10 points
Moderate roughness, moderately noncontinuous, moderate lack of definition: 5 points
Significant roughness, noncontinuous, lack of definition: 0 point
Margin placement (5 points) Equigingivally or not more than 0.5 mm subgingivally: 5 points
Not more than 1 mm subgingivally or 0.5 mm supragingivally: 3 points
More than 1 mm subgingivally or 0.5 mm supragingivally: 0 point
Adjacent tooth injury (10 points) Unaffected: 10 points
Minimally damaged: 5 points
Severely damaged: 0 point
Preparation time (10 points) ≤20 min: 10 points
20-25 min: 6 points
25-30 min: 3 points
> 30 min: 0 point

Figure 1

Learning curve for the mean scores of 4 tooth preparations"

Figure 2

Predicted learning curve of 30 tooth preparations"

Table 2

Comparison between the qualified standard score and the predicted scores of 30 tooth preparations"

Trial times Predicted score, M (P25, P75) P value
1 62.50 (57.44, 69.83) 0.002
2 70.56 (66.97, 73.44) 0.005
3 73.20 (70.52, 77.13) 0.005
4 75.40 (72.14, 81.44) 0.028
5 76.21 (73.59, 83.04) 0.136
6 76.86 (74.80, 84.01) 0.583
7 77.40 (75.83, 85.57) 0.583
8 77.88 (76.60, 86.81) 0.433
9 78.65 (77.31, 87.91) 0.388
10 79.35 (77.98, 88.91) 0.388
11 79.99 (78.54, 89.82) 0.347
12 80.58 (78.94, 90.65) 0.182
13 81.12 (79.35, 91.42) 0.158
14 81.62 (79.78, 92.14) < 0.050*
15 82.09 (80.18, 92.81) 0.019
16 82.59 (80.55, 93.44) 0.012
17 83.08 (80.91, 94.04) 0.008
18 83.55 (81.23, 94.61) 0.005
19 83.99 (81.54, 95.14) 0.004
20 84.42 (81.82, 95.66) 0.004
21 84.82 (82.07, 96.15) 0.003
22 85.21 (82.31, 96.62) 0.003
23 85.55 (82.53, 97.16) 0.002
24 85.76 (82.72, 97.20) 0.002
25 85.96 (82.93, 97.23) 0.002
26 86.16 (83.12, 97.26) 0.002
27 86.34 (83.30, 97.29) 0.002
28 86.52 (83.48, 97.34) 0.002
29 86.69 (83.65, 97.43) 0.002
30 86.86 (83.81, 97.51) 0.002
1 周永胜, 江泳, 徐军, 等. 仿真头颅模型在口腔修复学临床前期教学中的应用[J]. 中华医学教育杂志, 2006, 26 (3): 74- 76.
doi: 10.3760/cma.j.issn.1673-677X.2006.03.031
2 刘星纲, 刘利霞, 魏永杰. 浅析研究生修复实习中牙体预备的学习曲线[J]. 北京口腔医学, 2018, 26 (4): 236- 237.
3 Wright TP . Factors affecting the cost of airplanes[J]. J Astronaut Sci, 1936, 3 (4): 122- 128.
4 Sun TM , Lan TH , Pan CY , et al. Dental implant navigation system guide the surgery future[J]. Kaohsiung J Med Sci, 2018, 34 (1): 56- 64.
doi: 10.1016/j.kjms.2017.08.011
5 Lim JH , Park JM , Kim M , et al. Comparison of digital intraoral scanner reproducibility and image trueness considering repetitive experience[J]. J Prosthet Dent, 2018, 119 (2): 225- 232.
doi: 10.1016/j.prosdent.2017.05.002
6 Ben-Gal G , Katorza L , Weiss EI , et al. Testing motor learning curves among dental students[J]. J Dent Educ, 2017, 81 (10): 1171- 1178.
doi: 10.21815/JDE.017.076
7 Son K , Lee WS , Lee KB . Prediction of the learning curves of 2 dental CAD software programs[J]. J Prosthet Dent, 2019, 121 (1): 95- 100.
doi: 10.1016/j.prosdent.2018.01.004
8 Son K , Lee KB . Prediction of learning curves of 2 dental CAD software programs, part 2: Differences in learning effects by type of dental personnel[J]. J Prosthet Dent, 2020, 123 (5): 747- 752.
doi: 10.1016/j.prosdent.2019.05.026
9 Goodacre CJ , Campagni WV , Aquilino SA . Tooth preparations for complete crowns: An art form based on scientific principles[J]. J Prosthet Dent, 2001, 85 (4): 363- 376.
doi: 10.1067/mpr.2001.114685
10 赵铱民. 口腔修复学[M]. 7版 北京: 人民卫生出版社, 2012: 96- 97.
11 周永胜. 口腔修复学[M]. 3版 北京: 北京大学医学出版社, 2020: 87- 89.
12 Al Amri MD , Sherfudhin HR , Habib SR . Effects of evaluator's fatigue and level of expertise on the global and analytical evaluation of preclinical tooth preparation[J]. J Prosthodont, 2018, 27 (7): 636- 643.
doi: 10.1111/jopr.12558
13 李坚, 李泽. 学习曲线与进步函数: 考虑技术进步因素的优化方法[J]. 水利电力机械, 1997, (1): 3- 9.3-9, 11
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