123 research outputs found
Appendix_C_online_supp – Supplemental material for Exploring the Benefits of Transformations in Health Utility Mapping
No full textSupplemental material, Appendix_C_online_supp for Exploring the Benefits of Transformations in Health Utility Mapping by Nicholas Mitsakakis, Karen E. Bremner, George Tomlinson and Murray Krahn in Medical Decision Making</p
Appendix_D_online_supp – Supplemental material for Exploring the Benefits of Transformations in Health Utility Mapping
No full textSupplemental material, Appendix_D_online_supp for Exploring the Benefits of Transformations in Health Utility Mapping by Nicholas Mitsakakis, Karen E. Bremner, George Tomlinson and Murray Krahn in Medical Decision Making</p
Appendix_A_online_supp – Supplemental material for Exploring the Benefits of Transformations in Health Utility Mapping
No full textSupplemental material, Appendix_A_online_supp for Exploring the Benefits of Transformations in Health Utility Mapping by Nicholas Mitsakakis, Karen E. Bremner, George Tomlinson and Murray Krahn in Medical Decision Making</p
Appendix_B_online_supp – Supplemental material for Exploring the Benefits of Transformations in Health Utility Mapping
No full textSupplemental material, Appendix_B_online_supp for Exploring the Benefits of Transformations in Health Utility Mapping by Nicholas Mitsakakis, Karen E. Bremner, George Tomlinson and Murray Krahn in Medical Decision Making</p
supplementary_table2_online_supp – Supplemental material for Probabilistic Graphical Modeling for Estimating Risk of Coronary Artery Disease: Applications of a Flexible Machine-Learning Method
No full textSupplemental material, supplementary_table2_online_supp for Probabilistic Graphical Modeling for Estimating Risk of Coronary Artery Disease: Applications of a Flexible Machine-Learning Method by Alind Gupta, Justin J. Slater, Devon Boyne, Nicholas Mitsakakis, Audrey Béliveau, Marek J. Druzdzel, Darren R. Brenner, Selena Hussain and Paul Arora in Medical Decision Making</p
supplementary_table1_online_supp – Supplemental material for Probabilistic Graphical Modeling for Estimating Risk of Coronary Artery Disease: Applications of a Flexible Machine-Learning Method
No full textSupplemental material, supplementary_table1_online_supp for Probabilistic Graphical Modeling for Estimating Risk of Coronary Artery Disease: Applications of a Flexible Machine-Learning Method by Alind Gupta, Justin J. Slater, Devon Boyne, Nicholas Mitsakakis, Audrey Béliveau, Marek J. Druzdzel, Darren R. Brenner, Selena Hussain and Paul Arora in Medical Decision Making</p
sj-docx-1-mdm-10.1177_0272989X221099493 – Supplemental material for Noninferiority Margin Size and Acceptance of Trial Results: Contingent Valuation Survey of Clinician Preferences for Noninferior Mortality
No full textSupplemental material, sj-docx-1-mdm-10.1177_0272989X221099493 for Noninferiority Margin Size and Acceptance of Trial Results: Contingent Valuation Survey of Clinician Preferences for Noninferior Mortality by Sandra Pong, Robert A. Fowler, Nicholas Mitsakakis, Srinivas Murthy, Jeffrey M. Pernica, Elaine Gilfoyle, Asha Bowen, Patricia Fontela, Winnie Seto, Michelle Science, James S. Hutchison, Philippe Jouvet, Asgar Rishu and Nick Daneman in Medical Decision Making</p
Acute pain after total hip arthroplasty does not predict the development of chronic postsurgical pain 6 months later
No full textPurpose
Much remains unknown about the relationship between acute postoperative pain and the development of pathologic chronic postsurgical pain (CPSP). The purpose of this project was to identify the extent to which maximum pain scores on movement over the first two days after total hip arthroplasty predicted the presence of chronic pain 6 months later after controlling for potentially important covariates.
Methods
The sample comprised 82 of 114 patients who participated in a double-blinded randomized controlled trial in which all patients received acetaminophen 1 g p.o., celecoxib 400 mg p.o., and dexamethasone 8 mg i.v., 1–2 h preoperatively. In addition, patients received gabapentin (GBP) 600 mg (G2) or placebo (G1 and G3) 2 h prior to surgery [G1: placebo/placebo (n = 38); G2: GBP/placebo (n = 38); G3: placebo/GBP (n = 38)]. In the PACU, patients received gabapentin 600 mg (G3) or placebo (G1 and G2). Follow-up data from the 82 patients who were contacted by telephone 6 months postsurgery were used for the current study.
Results
Maximal movement-evoked pain intensity over the first two postoperative days (P = 0.38) failed to predict the presence of CPSP 6 months later after controlling for age (P = 0.09), treatment group (P = 0.91), and cumulative morphine consumption (P = 0.8) (multivariate logistic regression likelihood ratio test against the intercept only model P = 0.59).
Conclusion
Neither maximum movement-evoked acute pain, nor any other factor measured, predicted the presence of CPSP at 6 months. Further research is needed to identify risk factors for CPSP after total hip arthroplasty
Beyond case fatality rate: using potential impact fraction to estimate the effect of increasing treatment uptake on mortality
No full textThe Impact of the Underlying Risk in Control Group and Effect Measures in Non-Inferiority Trials With Time-to-Event Data: A Simulation Study
No full text- …
