Light, Sleep Duration, and Melatonin Among Rotating Shift Nurses
Bibliographic record
Abstract
PP-30-162 Background/Aims: The International Agency for Research on Cancer has classified shift work involving circadian disruption as a probable carcinogen. While the exact biologic mechanism for this relationship is unclear, the main hypothesis involves melatonin, a hormone produced in a pattern following circadian rhythms. The purpose of this research is to examine the influence of light at night exposure on peak melatonin levels among rotating shift nurses. Methods: One hundred twenty-four nurses at Kingston General Hospital working a rotating shift schedule (2 days, 2 nights, 5 days off) were recruited. Each participation session took place over a 48-hour period, during which nurses were asked to wear a light data logger and provide 2 urine and 4 saliva samples. Melatonin levels were assessed over a 24-hour period that covered either the first day or the second night shift of the rotation pattern, with the morning urine sample in both shift groups used to assess peak urinary melatonin levels. Mean light intensity from 12 am to 5 am was assessed from light data loggers during the 24 hours of melatonin assessment. Results: A total of 118 nurses completed the first 2 data collection periods. Mean light intensity from 12 am to 5 am was significantly higher (P < 0.0001) when nurses were working a night shift, and mean sleep duration was significantly shorter after the night shift (P = 0.005). Pilot work in this population suggested an inverse association between light intensity and melatonin levels. Multivariate analyses will be presented that allow us to characterize the relationship between light exposure and melatonin levels in this population of nurses working rotating shifts. Conclusion: Our research directly assesses the relationship between light exposure and melatonin, and will contribute evidence regarding the plausibility of melatonin as the biologic pathway linking shift work with cancer.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".