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Record W2023705571 · doi:10.1093/qjmed/hcn179

Anton Chekhov's MD Thesis

2008· article· en· W2023705571 on OpenAlexaboutno aff
Dorothy Galton

Bibliographic record

VenueQJM · 2008
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsGreeksCraftQuarter (Canadian coin)ClassicsHistoryLiteratureArtVisual arts

Abstract

fetched live from OpenAlex

If you had to choose the world's five greatest dramatists who would they be? Shakespeare and Moliere would figure highly on most people's list. Then perhaps the Ancient Greeks, Aeschylus and Sophocles (or Euripides); and then in more modern times there may be a toss-up between Ibsen and Chekhov for fifth place. I would personally go for Chekhov—he is less harrowing to watch than Ibsen. There is usually a Chekhov play showing somewhere in the United Kingdom every year. Ivanov is currently showing in London. If you had to choose the world's five greatest short story writers who would they be? The number to choose (Maupassant, Henry James, Graham Greene, etc.) seems to be much larger than for dramatists perhaps it is an easier craft to ply. But again Chekhov would figure highly on many people's list. It must be quite unique for a practicing physician to get on two such short lists. Anton P. Chekhov (1860–1904) qualified in medicine from Moscow University in 1884. While a student he had worked in the Vozkresenk Hospital and after taking his degree was employed on the staff of the Zvenigorod Hospital. He went into practice and from 1892–93 he was medical superintendent of the Melikhovo district near Moscow. In 1890, Chekhov wanted to join the lecturer staff of a medical school and to do this he had to write a thesis towards the degree of Doctor of Medical Sciences at Moscow University. He completed the thesis in 1895. The project was to undertake a medico-social study of Sakhalin Island. His aim as a physician was to study the social conditions of the Island to see how …

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.754

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.301
Teacher spread0.264 · 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 designNot applicable
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
Published2008
Admission routes1
Has abstractyes

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