Bibliographic record
Abstract
In the first issue of 2018, Ali et al1 look at how we can identify pregnancies with a low risk of asthma exacerbation. Data from over a thousand pregnancies were analysed. They identified that a lack of exacerbations pre-pregnancy, no controller medication and clinically stable asthma were associated with less than a 1% chance of exacerbation. This allows clinicians to direct enhanced surveillance towards those that do have a significant risk of having an exacerbation. It is well known that atopic dermatitis and allergic sensitisation are associated with the development of allergic diseases.2, 3 The expression of these early life atopic manifestations varies between individuals. Dharma et al4 set out to determine whether these impact on the risk of developing future allergic diseases. They applied unsupervised latent class analysis to determine the different patterns of allergic sensitisation and atopic dermatitis in infants from the Canadian Healthy Infant Longitudinal Development (CHILD) Study. Five distinct patterns found were as follows: a large healthy group and then smaller groups with atopic dermatitis, inhalant sensitisation, transient sensitisation and persistent sensitisation (Figure 1). Each had a different risk of developing atopic diseases with the persistent sensitisation group perhaps the predictably most at risk. Questions remain, are these reproducible and what might drive the development of these groups? The number of components for common food allergens increases all the time.5 But do these components have any clinical meaning? Blankestijn et al6 have looked at whether Ara h 7 is useful in diagnosing peanut allergy. They compare it to Ara h 2 and Ara h 6 isoforms using sera from 40 peanut-tolerant and 40 peanut-allergic individuals based on food challenge (Figure 2). Ara h 7, Ara h 2 and Ara h 6 have comparable utilities for diagnosing peanut allergy. Most individuals were co-sensitized to all three 2S albumins Ara h 2, 6 or 7, but some are only sensitized to one leading to the possibility of misdiagnosis when only one 2S albumin is used in a diagnostic algorithm.7
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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; both teacher heads agree on what is shown here.
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".