The Influence of Artificial Light at Night on Asthma and Allergy, Mental Health, and Cancer Outcomes: A Systematic Scoping Review Protocol
Bibliographic record
Abstract
Artificial light at night (ALAN) exposure is associated with the disruption of human circadian processes. Through numerous pathophysiological mechanisms such as melatonin dysregulation, it is hypothesised that ALAN exposure is involved in asthma and allergy, mental illness, and cancer outcomes. There are numerous existing studies considering these relationships; however, a critical appraisal of available evidence on health outcomes has not been completed. Due to the prevalence of ALAN exposure and these outcomes in society, it is critical that current evidence of their association is understood. Therefore, this systematic scoping review will aim to assess the association between ALAN exposure and asthma and allergy, mental health, and cancer outcomes. This systematic scoping review will be conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses statement. We will search bibliographic databases, registries, and references. We will include studies that have described potential sources of ALAN exposure (such as shift work or indoor and outdoor exposure to artificial light); have demonstrated associations with either allergic conditions (including asthma), mental health, or cancer-related outcomes; and are published in English in peer-reviewed journals. We will conduct a comprehensive literature search, title and abstract screening, full-text review, and data collection and analysis for each outcome separately.
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 | 0.069 | 0.080 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.019 | 0.018 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.062 | 0.007 |
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".