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Impact of sunlight on the age of onset of bipolar disorder

2012· article· en· W2114113690 on OpenAlexafffund
Michael Bauer, Tasha Glenn, Martin Alda, Ole A. Andreassen, Raffaella Ardau, Frank Bellivier, Michael Berk, Thomas Bjella, Letizia Bossini, Maria Del Zompo, Seetal Dodd, Andrea Fagiolini, Mark A. Frye, Ana González‐Pinto, Chantal Henry, Flávio Kapczinski, Sebastian Kliwicki, Barbara König, Maurício Kunz, Beny Lafer, Carlos López‐Jaramillo, Mirko Manchia, Wendy Marsh, Mónica Martínez‐Cengotitabengoa, Ingrid Melle, Gunnar Morken, Rodrigo Muñoz, Fabiano G. Nery, Claire O’Donovan, Andrea Pfennig, Danilo Quiroz, Natalie Rasgon, Andreas Reif, Janusz Rybakowski, Kemal Sagduyu, Christian Simhandl, Carla Torrent, Eduard Vieta, Mark Zetin, Peter C. Whybrow

Bibliographic record

VenueBipolar Disorders · 2012
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health Research
KeywordsBipolar disorderSunlightPsychologyPsychiatryAge of onsetMedicineInternal medicineAstronomyPhysicsMood

Abstract

fetched live from OpenAlex

Bauer M, Glenn T, Alda M, Andreassen OA, Ardau R, Bellivier F, Berk M, Bjella TD, Bossini L, Del Zompo M, Dodd S, Fagiolini A, Frye MA, Gonzalez‐Pinto A, Henry C, Kapczinski F, Kliwicki S, König B, Kunz M, Lafer B, Lopez‐Jaramillo C, Manchia M, Marsh W, Martinez‐Cengotitabengoa M, Melle I, Morken G, Munoz R, Nery FG, O’Donovan C, Pfennig A, Quiroz D, Rasgon N, Reif A, Rybakowski J, Sagduyu K, Simhandl C, Torrent C, Vieta E, Zetin M, Whybrow PC. Impact of sunlight on the age of onset of bipolar disorder. Bipolar Disord 2012: 14: 654–663. © 2012 The Authors. Journal compilation © 2012 John Wiley & Sons A/S. Objective: Although bipolar disorder has high heritability, the onset occurs during several decades of life, suggesting that social and environmental factors may have considerable influence on disease onset. This study examined the association between the age of onset and sunlight at the location of onset. Method: Data were obtained from 2414 patients with a diagnosis of bipolar I disorder, according to DSM‐IV criteria. Data were collected at 24 sites in 13 countries spanning latitudes 6.3 to 63.4 degrees from the equator, including data from both hemispheres. The age of onset and location of onset were obtained retrospectively, from patient records and/or direct interviews. Solar insolation data, or the amount of electromagnetic energy striking the surface of the earth, were obtained from the NASA Surface Meteorology and Solar Energy (SSE) database for each location of onset. Results: The larger the maximum monthly increase in solar insolation at the location of onset, the younger the age of onset (coefficient= −4.724, 95% CI: −8.124 to −1.323, p = 0.006), controlling for each country’s median age. The maximum monthly increase in solar insolation occurred in springtime. No relationships were found between the age of onset and latitude, yearly total solar insolation, and the maximum monthly decrease in solar insolation. The largest maximum monthly increases in solar insolation occurred in diverse environments, including Norway, arid areas in California, and Chile. Conclusion: The large maximum monthly increase in sunlight in springtime may have an important influence on the onset of bipolar disorder.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.269
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations53
Published2012
Admission routes2
Has abstractyes

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