Wavelength-resolved measures of outdoor artificial light at night and breast cancer risk
Bibliographic record
Abstract
BACKGROUND: Outdoor artificial light at night (ALAN) may increase breast cancer risk by suppressing melatonin secretion, an effect influenced by light intensity and wavelength. OBJECTIVES: We evaluated the association of multiple ALAN measures with breast cancer risk. METHODS: We pooled data from two cohort studies (baseline: 2009-2016; n = 24,793 female participants, 674 breast cancer cases). ALAN exposures at residential addresses were estimated using images taken from the International Space Station (2011-2013; ∼30-meter spatial resolution). Measures included average visual radiance (i.e., brightness), average melatonin suppression index (MSI), which quantifies the extent to which light suppresses melatonin, and their product (average impact MSI). Cox proportional hazards regression, adjusted for breast cancer risk factors and built environment features, was used to generate hazard ratios with 95 % confidence intervals (CI) for the associations between ALAN and breast cancer incidence. We also explored associations with breast cancer subtypes (invasive ductal, luminal A, postmenopausal) and among participants self-reporting light entering their rooms while sleeping. RESULTS: No statistically significant associations were observed in the overall study population. Among women reporting light entering their rooms, statistically significant associations of average impact MSI with breast cancer risk, overall and across each subtype, were observed. For example, those in the highest versus lowest tertile of average impact MSI had a 1.53-fold increased hazard of overall breast cancer (95 % CI: 1.18-1.98). DISCUSSION: Our findings suggest that outdoor ALAN is associated with increased risk of breast cancer and that both intensity and wavelength of light should be considered when evaluating ALAN exposures.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".