Bibliographic record
Abstract
BACKGROUND: It is apparent that many fertility patients consider multiple birth an ideal treatment outcome. We wished to evaluate the desire for multiple birth among patients, and the effect of patient demographics and recognition of the increased fetal risks of multiple pregnancy on this desire. METHODS: This was a prospective questionnaire study completed by 801 male and female infertility patients attending a tertiary level Canadian university fertility clinic. Two logistic regression analyses were performed with desire for multiple birth with next fertility treatment and recognition of the increased fetal risks of multiple pregnancy as the dependent variables. RESULTS: 41% of patients desired a multiple birth. Increasing duration of infertility or previous assisted reproductive treatment increased, and having previous children or recognition of the increased fetal risks decreased, this desire. Patient age or sex did not affect desire for multiple birth. Previous assisted reproductive treatment was associated with increased recognition of the fetal risks of multiple pregnancy. CONCLUSIONS: A significant proportion of fertility patients considers multiple birth an ideal treatment outcome. Recognition of the increased fetal risks of multiple pregnancy significantly reduced this desire. Patient education may play an important role in assisting physicians in the quest to reduce the contribution of assisted reproductive treatment to multiple births and their attending complications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".