Thursday, April 10, 2014

Teeth & Implants: The Rest of the Story

At the upcoming Inner Space Seminar on Friday, April 11th, 2014, Dr. Hessam Nowzari will present a balanced approach to the discussion regarding teeth and implants.

  • What does the research show regarding the long term success of restored teeth and dental implants?
  • What effect does corporate marketing have on the dental educational system and dentistry as a whole?
  • What is the quality of research that is being published in our dental journals and what effect does that have on our profession?
  • How do we evaluate the quality of research to help us make important clinical decisions for our patients?
  • What are the important considerations when deciding to retain a tooth or replace with an implant?

As a periodontist, implant surgeon, former director of graduate periodontics at USC, and world renowned lecturer and researcher, he is uniquely qualified to lead this discussion in a responsible, scientific and unbiased manner. 

UPDATE:  This morning we are inviting all of the participants of the seminar, to share their thoughts and impressions of the presentation throughout the presentation.


Anonymous said...

I learned about attrition and publication bias Kathy Pearce

Anonymous said...

i learned to not believe everything you read including scientific studies.

Leah Brown said...

I love Dr Nowzari's candor and passion! I am interested to know why there is a increase in the agenesis of #7 and #10... Thank you Superstition Endo for always hosting such excellent continuing education events!

The Endo Blog said...

When you read an article: look at the design, make sure it's randomized, look for biases. The author should identify the biases if they are honest.

Bias in any study should be categorized as "low risk", "high risk" & "unclear risk".

The Endo Blog said...

Don't trust the data of a study where the data was collected and analyzed by the same person.

Their are 385 identified biases that can affect the outcome of any study.

Bias is a systematic deviation from the true value. It is not the same as random error.

Bias is a serious problem that cannot be reduced by increasing the sample size or averaging

Small studies have large amount of random error. So studies with small sizes are less reliable.