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  2. Pregnancy rate - Wikipedia

    en.wikipedia.org/wiki/Pregnancy_rate

    Pregnancy rate is the success rate for getting pregnant.It is the percentage of all attempts that leads to pregnancy, with attempts generally referring to menstrual cycles where insemination or any artificial equivalent is used, which may be simple artificial insemination (AI) or AI with additional in vitro fertilization (IVF).

  3. Bishop score - Wikipedia

    en.wikipedia.org/wiki/Bishop_score

    Bishop score, also Bishop's score or cervix score, is a pre-labor scoring system to assist in predicting whether induction of labor will be required. [1] It has also been used to assess the likelihood of spontaneous preterm delivery. [2]

  4. Estimated date of delivery - Wikipedia

    en.wikipedia.org/wiki/Estimated_date_of_delivery

    A median of 288 days (274 days from the date of ovulation) for first-time mothers and 283 days (269 days from the date of ovulation) for mothers with at least one previous pregnancy was found by a 1990 study of 114 white, private-care patients with uncomplicated pregnancies and spontaneous labor. The authors suggest that excluding pregnancies ...

  5. Likelihood-ratio test - Wikipedia

    en.wikipedia.org/wiki/Likelihood-ratio_test

    In statistics, the likelihood-ratio test is a hypothesis test that involves comparing the goodness of fit of two competing statistical models, typically one found by maximization over the entire parameter space and another found after imposing some constraint, based on the ratio of their likelihoods.

  6. Fertility testing - Wikipedia

    en.wikipedia.org/wiki/Fertility_testing

    Estimations of ovarian reserve help to determine the likelihood of pregnancy by In vitro fertilization (IVF). AMH testing is considered to be one of the most accurate estimates of ovarian reserve, can be used for assessment at any point in the menstrual cycle, and is non-invasive.

  7. Likelihood ratios in diagnostic testing - Wikipedia

    en.wikipedia.org/wiki/Likelihood_ratios_in...

    If the likelihood ratio for a test in a population is not clearly better than one, the test will not provide good evidence: the post-test probability will not be meaningfully different from the pretest probability. Knowing or estimating the likelihood ratio for a test in a population allows a clinician to better interpret the result. [7]