Predictors of male microchimerism
Bibliographic record
Abstract
The association between microchimerism acquired primarily through pregnancy and later disease is of increasing scientific interest. Because this line of research is new and little is known about the nature of microchimerism, studies of microchimerism are potentially vulnerable to error from confounding and reverse causation. To address the issue of confounding, we conducted an analysis of predictors of male microchimerism in 272 female participants of the Danish Diet, Cancer and Health cohort. Buffy coat DNA was tested for Y chromosome presence as a marker of male microchimerism. First, we used logistic regression and thereafter random forest modeling to evaluate the ability of a range of reproductive, lifestyle, hospital or clinic visit history, and other variables to predict whether women tested positive for male microchimerism. We found some indication that current use of contraceptive pills and hormone replacement therapy reduced the odds of testing positive for male microchimerism. However, prediction of male microchimerism presence was poor based on the available variables. Studies of the possible role of male microchimerism in maternal health and disease are therefore unlikely to be heavily confounded by the variables examined in the present investigation. More research focused on acquisition, retention and clearing of male cells in the maternal circulation is needed.
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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 | 0.005 |
| 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 | 0.001 |
| 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".