Factors Affecting Response to Infertility Treatment: Case of Iran
Bibliographic record
Abstract
INTRODUCTION: Infertility affects both women and men in all the countries. Infertility often has profound long-term or short-term impacts on the people involved and puts them at risk of familial and social pressures. According to WHO estimates, between 8% and 12% of all the couples worldwide experience some form of infertility during their reproductive life, i.e. 50‒80 million people are affected. The aim of this study was to evaluate the response to infertility treatment by taking into account factors such as age, hirsutism, menstruation and galactose among women in Kerman. METHODOLOGY: Of a total of 300 patient files evaluated 220 cases were flawless, of which the study factors were recorded. These data were estimated by Logit model. The dependent variable was the response to treatment (0 and 1) and the independent variables included age of men and women, hirsutism, menstruation, galactose, duration of the period no preventive measures were used and body mass index. After entering the data, model output was analyzed by using the STATA software. RESULTS: The results showed that of all the model variables, female age (prob=0.0065), menstruation (prob=0.04), hirsutism (prob=0.02), marriage age (in months) (prob=0.02) and BMI were significant and other variables were not significant. McFadden analysis for goodness of fit was 0.92. CONCLUSION: The study results showed that women should pay more attention to variables such as BMI, menstruation quality (regular and irregular) and aging because clinical disregard of any of the above can have a significant impact on the individual's fertility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".