Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Predictive Model of Intraoperative Pain during Endodontic Treatment: Prospective Observational Clinical Study
Date
2016-01-01
Author
KAYAOĞLU, GÜVEN
Gurel, Mugem
Saricam, Esma
İLHAN, MUSTAFA NECMİ
İlk Dağ, Özlem
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
220
views
0
downloads
Cite This
Introduction: This observational study sought to assess the incidence of intraoperative pain (10P) among patients receiving endodontic treatment and to construct a model for predicting the probability of 10P. Methods: All patients attending the endodontic training clinic at Gazi University, Ankara, Turkey, during the spring term of 2014 were examined (N = 2785 patients; observation completed in 1435 patients; male: 628, female: 807; mean age: 39 years; 1655 teeth total). Demographic and clinical variables were recorded for patients requiring primary endodontic treatment. Local anesthesia was administered and routine endodontic treatment commenced. After the working length was established, each patient was asked to report any pain according to a visual analog scale. Supplementary local infiltration anesthesia was administered if necessary. If pain continued despite supplementary anesthesia, then the pain score was immediately assessed. A visual analog scale score corresponding to more than mild pain indicated 10P. A predictive model was constructed with multiple logistic regression analysis from the data of 85% of cases, with the remaining 15% of cases being used to test the external validity of the model. Results: The incidence of 1013 was 6.1% (101/1655 cases). One tooth from each patient was randomly selected, with 1435 teeth being retained for further analysis. A multiple logistic regression model was constructed with the variables age, tooth type, arc, pulpal diagnosis, pain present within the previous 24 hours, and anesthetic solution (P <.05). Good fits were obtained for the final model and external control, with a correct classification rate (efficiency) of 0.78, sensitivity (true positive rate) of 0.63, and specificity (true negative rate) of 0.79 for the external control. Conclusions: A successful predictive model of 10P was constructed with demographic and clinical variables.
Subject Keywords
Dental anesthesia
,
Endodontics
,
Forecasting
,
Pain assessment
,
Root canal treatment
,
Root canal therapy
URI
https://hdl.handle.net/11511/45624
Journal
JOURNAL OF ENDODONTICS
DOI
https://doi.org/10.1016/j.joen.2015.09.021
Collections
Department of Statistics, Article
Suggestions
OpenMETU
Core
Predicting intraoperative pain in emergency endodontic patients: clinical study
Yucel, Olga; Ekici, Mugem Asli; İlk Dağ, Özlem; İLHAN, MUSTAFA NECMİ; KAYAOĞLU, GÜVEN (2018-01-01)
This prospective observational study sought to investigate the incidence of intraoperative pain (IOP) among emergency endodontic patients and to construct an IOP prediction model that includes preoperative pain level (PPL). All patients who underwent emergency endodontic treatment at Gazi University, Ankara, Turkey, during the spring term of 2016 were considered for inclusion in the study. Demographic and clinical variables and PPL were recorded. Local anesthesia was provided to all patients before beginnin...
Analysis of factors affecting baseline SF-36 Mental Component Summary in Adult Spinal Deformity and its impact on surgical outcomes
Mmopelwa, Tiro; Ayhan, Selim; Yuksel, Selcen; Nabiyev, Vugar; Niyazi, Asli; Pellise, Ferran; Alanay, Ahmet; Sanchez Perez Grueso, Francisco Javier; Kleinstuck, Frank; Obeid, Ibrahim; Acaroglu, Emre (AVES Publishing Co., 2018-5)
Objectives: To identify the factors that affect SF-36 mental component summary (MCS) in patients with adult spinal deformity (ASD) at the time of presentation, and to analyse the effect of SF-36 MCS on clinical outcomes in surgically treated patients. Methods: Prospectively collected data from a multicentric ASD database was analysed for baseline parameters. Then, the same database for surgically treated patients with a minimum of 1-year follow-up was analysed to see the effect of baseline SF-36 MCS on t...
Bivariate random effects and hierarchical meta-analysis of summary receiver operating characteristic curve on fine needle aspiration cytology
Erte, İdil; Baykal, Nazife; Akçil, Mehtap; Department of Medical Informatics (2011)
In this study, meta-analysis of diagnostic tests, Summary Receiver Operating Characteristic (SROC) curve, bivariate random effects and Hierarchical Summary Receiver Operating Characteristic (HSROC) curve theories have been discussed and accuracy in literature of Fine Needle Aspiration (FNA) biopsy that is used in the diagnosis of masses in breast cancer (malignant or benign) has been analyzed. FNA Cytological (FNAC) examination in breast tumor is, easy, effective, effortless, and does not require special tr...
Random effects’ distribution assumption on joint mixed modelling
Özdemir, Celal Oğuz; Kalaylıoğlu Akyıldız, Zeynep Işıl; Department of Statistics (2018)
Joint mixed model is an appealing approach in medical research where it is critical to estimate the odds of a fatal complication that occurs to a patient given the covariate profile such as a risk factor observed over time. For this kind of estimation, joint mixed model is used. In the standard Bayesian analysis of the model, the error variance and random effects’ variance-covariance matrix are apriori modeled independently with Inverse-Gamma and Inverse-Wishart distributions respectively. Recently however,...
Patterns of Depression in Medical Patients and Their Relationship with Causal Attrihutions for Illness
Karanci, Nuray A. (S. Karger AG, 1988)
he present study investigated the factor structure of the Beck Depression Inventory (BDI), and causal attributions for the development of illness in a sample of 102 inpatients of a thoracic surgery department, with the main objective of examining the power of causal attributions and functional support in predicting different factors derived from the BDI. The results revealed that the BDI clusters into affective/motivational, somatic/vegetative, self-blame and self-punitiveness dimensions. Causal attribution...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
G. KAYAOĞLU, M. Gurel, E. Saricam, M. N. İLHAN, and Ö. İlk Dağ, “Predictive Model of Intraoperative Pain during Endodontic Treatment: Prospective Observational Clinical Study,”
JOURNAL OF ENDODONTICS
, pp. 36–41, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/45624.