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Record W3087402220 · doi:10.1017/s1047951120002978

Doctor Gachet, in the kitchen, with the foxglove

2020· article· en· W3087402220 on OpenAlexaboutno aff
Richard D. Mainwaring, Stephanie Mainwaring

Bibliographic record

VenueCardiology in the Young · 2020
Typearticle
Languageen
FieldMedicine
TopicAlcoholism and Thiamine Deficiency
Canadian institutionsnot available
Fundersnot available
KeywordsPaintingMedicineQuarter (Canadian coin)Mental illnessPsychiatryGlobeArt historyArtHistoryMental healthOphthalmology

Abstract

fetched live from OpenAlex

Vincent van Gogh (1853-1890) is one of the most famous artists in the world. During his 10-year career as an artist, he created more than 850 paintings. These works of art are now displayed in museums around the globe. It is therefore even more surprising that van Gogh sold just one painting during his lifetime. Van Gogh is also well-known for his mental illness. In 1888, at the age of 35, he famously sliced off his left ear. This was followed by multiple mental collapses in early 1889, leading to his admission to a mental hospital. Despite living in the asylum, van Gogh continued to paint and created some of his most beautiful works of art during the year at Saint-Rémy. Tragically, he committed suicide in 1890 at the age of 37. Over the 130 years since his death, there has been much speculation about the underlying illness of Vincent van Gogh. Many of his contemporary physicians felt that he had a form of epilepsy as the cause of his sudden "attacks". By the last quarter of the 19th century, science and medicine were moving rapidly forward, and there were many medical conditions that had effective treatments. One example is the use of digoxin for the treatment of heart failure, and another is the discovery of potassium bromide for seizures. This paper provides an overview of van Gogh's mental illness, the treatments that were offered by his contemporaneous physicians, and the role that these factors may have influenced his paintings.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.096
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.268
Teacher spread0.235 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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