Development, Testing, and Calibration of LINCS: A New Microsimulation Model of Maternal and Fetal Cytomegalovirus Infection in the US
Bibliographic record
Abstract
ABSTRACT Background Congenital cytomegalovirus (cCMV) is a leading cause of birth defects and the most common cause of non-genetic sensorineural hearing loss in children. There is a lack of decision modeling frameworks that can project cytomegalovirus (CMV)-related patient outcomes and inform health policy. We created, tested, and calibrated a model of CMV acquisition and transmission in pregnancy using linked mother-infant dyads. Methods We developed the L inking IN fants and Mothers in C ytomegalovirus S imulation (LINCS) dyad-level Monte-Carlo microsimulation model of CMV infection among pregnant people and fetuses throughout pregnancy. We parameterized the model with data from the US, Canada and the EU from the existing literature, implemented rigorous code testing procedures, and calibrated a key set of parameters to match model output to external data on cCMV prevalence and symptom risk. Results A fully parameterized model for CMV among pregnant people and fetuses was developed, and the model code was confirmed to perform as specified. The calibration procedure identified parameter sets that generated model output closely matching the target values from the available data on cCMV prevalence and symptom risk. Conclusions The LINCS model’s ability to simulate the natural history of CMV infection during pregnancy was described and demonstrated, and the model was tested and calibrated to ensure proper functioning. Base case parameters were derived for CMV infection natural history to be used in future decision analyses of CMV testing and treatment strategies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".