Sentences with phrase «better predictive value»

Clinically useful guides, such as the Opioid Risk Tool, offer good predictive value for physicians.

Well, any amount of titer has a very good predictive value.

Not exact matches

Historians and sociologists have good reason for being skeptical that such a major change is imminent, and they are correct in questioning whether Jewish population surveys have much predictive value.

There has been some predictive value of these players going on to decent or better NBA careers.

Third trimester parameters demonstrated the best positive predictive value.

Nonclinical in vivo assessment of QT interval in cardiovascular safety pharmacology studies has limited predictive value as well, often necessitating a «Thorough QT» clinical study later in development.

said Judith K. Wolf, MD, Chief Medical Officer of Provista Diagnostics, Inc. «This study shows that, with an over 99 percent negative predictive value (NPV), clinicians can confidently use Videssa Breast to detect cancer in women with dense breasts and better determine when biopsy is truly warranted to assess suspicious findings.»

«Our research shows BDNF levels change considerably across pregnancy and provide predictive value for depressive symptoms in women, as well as poor fetal growth.

In British elder subjects waist was the best predictor of diabetes in women, whereas in males the predictive value of BMI and waist were comparable (25).

While the theory makes sense, any good biomarker worth its predictive value will also have the following characteristics:

There are a number of potential explanations for this finding, including a limited supply of effective teachers (it's rational to keep a mediocre teacher if the likely replacement will be no better), a lack of administrator ability to discern teacher quality (their observations are less predictive of value - added than those of outside observers), or a simple unwillingness to make the unpleasant decision of firing someone.

Moreover, notes Rothstein, while it's true that past value - added is a better predictor than are student perceptions, the actual predictive power of the former is quite low.

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If we accept that the crunch - free predictive model is a good predictor of game outcomes (and the extremely high correlation and tiny p - value suggest that it is), then the «crunch salvage hypothesis» tells us that we should expect that it should improve the outcomes of game projects where it is used at least to some tiny, observable extent... and the more it is used, the more it should improve game project outcomes.

The problem I have with it is its usefulness, as the speaker basically said that if the answer didn't match observations then that just meant the constraints were wrong (or possibly the prior - he did not insist on a uniform one) which implies it doesn't have any practical predictive value, as there seems no way of knowing which constraints are the right ones other than by checking if the answer looks good... Rumour has it that one of the later speakers may be going to say it is not useful for our sort of climate research for some reason... it will all be on video at some later date for those who are interested.

In particular, Mann et al are correct to state that a negative value of RE can be statistically significant, they were well aware that this does not equate to reconstructive skill (the quote Alan provides from MBH98 continues: «In this sense, statistically significant negative values of b might still be considered questionable in their predictive or reconstructive skill.»)

So one has to wait and see if one's model, adjusted and improved to better fit the past up to the present, actually has any predictive value.

If the calibration has any predictive value, one would expect it to do better than just the sample average over the validation period and, for this reason, CE is a particularly useful measure.

Also, the most strategic law firms — the ones that view predictive coding as a key tool in helping them add value and better serve clients — are driving adoption among competing firms.

By recognizing both the value and limitations of predictive studies, professionals and the public alike will be served best.»