Optimizing oral immunotherapy to cow milk protein: a decision analysis
Bibliographic record
Abstract
Oral immunotherapy (OIT) to cow milk protein (CMP) allows some children with cow milk allergy (CMA) to outgrow their allergy sooner, but increases their initial risk of anaphylaxis. We used Markov transition models to compare the expected lifetime gain in quality-adjusted life years (QALYs) of OIT to CMP versus strict avoidance of CMP. Models were run for base cases of 6- to 16-year-old children with CMA requiring strict CMP avoidance. Rates of transition to the partial or full desensitization and complete tolerance states, utilities for each state, and disutilities and durations of reactions were determined from the literature. Participants progressed through the OIT states in order but could regress to an earlier state or repeat OIT. For an 8-year-old child, OIT resulted in a 0.9 QALYs gain compared with strict avoidance; this benefit increased to 1.9 QALYs for a 16-year-old. Sensitivity analysis showed that OIT became the preferred strategy within 6 years of starting OIT. The models were sensitive to the state utilities, but not to the transition probabilities between states. Probabilities of reactions had to be over 10 times the literature-based estimates for OIT to no longer be the preferred strategy. Limitations of these models included the paucity of utility measures for children with CMA and the possible under-reporting of CMA-related reactions or death. For children with CMA, OIT offers improved QALYs and the benefits outweigh the risks within a few years. Determination of utilities for younger children with CMA will help to further address this question.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".