Nomogram to predict changes in semen parameters following clomiphene citrate therapy for males with abnormal semen parameters
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background: </ns4:bold> Clomiphene citrate (CC) is known to improve semen quality for men with infertility, but there are no published algorithms to predict the changes in an individual’s semen parameters following CC therapy. Since there are multiple options to treat men with infertility, a model to predict the outcomes of CC therapy would allow men to make a more informed decision on their treatment choices. </ns4:p> <ns4:p> <ns4:bold>Methods: </ns4:bold> This is a prospective study on a cohort of 121 infertile men being treated empirically with CC 25 mg every other day for a minimum of three months. Men were included if they did not have other active fertility therapies other than the use of supplements. Semen samples and a hormone profile (including total testosterone, follicle stimulating hormone (FSH) and leutinizing hormone (LH)) were provided prior to and at least three months after initiation of therapy. The patient age, pre-CC hormone values and semen parameters were used to develop a nomogram to predict the post-CC hormone values and semen parameters. The model was developed with predictors selected using backward selection methods by minimizing root mean square error evaluated on 500 bootstrap runs. A zero-inflated negative binomial modeling (ZINB) approach was used to model sperm concentration and sperm motility. A generalized linear model for the Gamma distribution was used to model testosterone. A linear model was used to model log transformed LH, FSH and semen volume. </ns4:p> <ns4:p> <ns4:bold>Results: </ns4:bold> Post-CC sperm parameters and hormone values were predicted by the pre-CC sperm concentration, hormone values and patient. Nomograms were developed to predict the outcomes of CC therapy based on pre-CC parameters. </ns4:p> <ns4:p> <ns4:bold>Conclusion: </ns4:bold> These new models will help physicians personalize care by predicting the outcomes of therapy and allow clinicians to tailor the treatment to the individual couple. </ns4:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".