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  2. 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]

  3. 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).

  4. Calendar-based contraceptive methods - Wikipedia

    en.wikipedia.org/wiki/Calendar-based...

    When used to avoid pregnancy, the standard days method has been estimated [22] to have perfect-use efficacy of 95% and typical-use efficacy of 88%. [ 20 ] [ 21 ] These figures are based on a 2002 study in Bolivia, Peru, and the Philippines of women of reproductive age having menstrual cycles between 26 and 32 days, [ 20 ] [ 23 ] : 505 and on a ...

  5. Estimated date of delivery - Wikipedia

    en.wikipedia.org/wiki/Estimated_date_of_delivery

    Here's the formula to calculate your Estimated Due Date using Naegele's rule : Date of Last Menstrual Period + 7 Days + 9 Calendar Months = Date of Estimated Date of Delivery. Example: LMP = 8 May 2020 +1 year = 8 May 2021 −3 months = 8 February 2021 +7 days = 15 February 2021

  6. Age and female fertility - Wikipedia

    en.wikipedia.org/wiki/Age_and_female_fertility

    Statistical analysis showed that the women in the 27–29 age group had significantly less chance on average of becoming pregnant than did the 19- to 26-year-olds. Pregnancy rates did not change notably between the 27–29 age group and the 30–34 age group, but dropped significantly for the 35–39 age group. [14]

  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]

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