A Novel Approach to Estimating Average Long-term Endotoxin Exposure for Children: The Endotoxin Exposure Matrix
Bibliographic record
Abstract
PP-30-121 Background/Aims: A major limitation to quantitative endotoxin exposure assessment is that it only effectively captures endotoxin exposure level at 1 time-point. Thus far, no feasible or economical method has been proposed to estimate average long-term endotoxin exposure. A potential solution to this difficulty is to borrow from occupational epidemiology and adapt the concepts of job exposure matrices to estimate cumulative endotoxin exposure, which acts as a proxy for average exposure over time, in a new exposure matrix—the Endotoxin Exposure Matrix (EEM). Methods: The EEM is designed to estimate the environmental (background) levels of endotoxin and incorporate intensifying factors (factors affecting background levels). These environmental factors (location of residence, degree of parental interaction, and household dust) are assigned an endotoxin exposure score in an a priori fashion (with the exception of household dust). Household levels of endotoxin will be measured from collected dust samples using the LAL Assay and converted into endotoxin exposure scores, which is assessed along a 3-point ordinal scale. This quantitative measure will also allow internal validation of the EEM. Exposure status for various intensifying endotoxin factors will act as multipliers for their corresponding environmental factors (eg, infrequent house cleaning increases the endotoxin exposure from household dust). To capture the long-term nature of exposure, duration of exposure (years lived) will be taken into account. Endotoxin exposure scores associated with the environmental factors will be multiplied by the duration of exposure and corresponding intensifying factors and summed to give an endotoxin exposure index (EEI). Results: As the EEI is semiquantitative, its interpretation is on an ordinal scale, such that a higher EEI value corresponds to a higher level of endotoxin exposure and the absolute value of the index is not meaningful. Conclusion: Therefore, the EEM allows average long-term endotoxin exposure to be estimated in a feasible and economical fashion.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".