Additional file 1 of Influence of light at night on allergic diseases: a systematic review and meta-analysis
Bibliographic record
Abstract
Additional file 1: Table S1. Comprehensive strategy of the initial search to identify studies considering the influence of light at night exposure on allergic diseases. Table S2. Comprehensive strategy of the updated search to identify studies considering the influence of light at night exposure on allergic diseases. Table S3. Summary of meta-analyses considering the association between light at night exposure and the odds of allergic diseases. Fig. S1. Funnel plot of the exposure-specific meta-analysis for the association between light at night exposure and the odds of allergic diseases. Fig. S2. Funnel plot of the outcome-specific meta-analysis for the association between light at night exposure and the odds of allergic diseases. Fig. S3. Risk of Bias In Non-randomized Studies – of Exposures (ROBINS-E) quality assessment of included studies considering the influence of light at night exposure on allergic diseases. Table S4. Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) criteria evidence table of meta-analyses considering the influence of light at night exposure on the odds of allergic diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.773 | 0.034 |
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".