Bibliographic record
Abstract
Arthur Conan Doyle, the creator of Sherlock Holmes, was an experienced physician who treated gouty patients. A gouty character appears in The Adventure of the Missing Three-Quarter, a Sherlock Holmes novel. This offers the possibility of discussing gout from the peculiar perspective of a medical writer in light of the historical-medical context of the time. This study was conducted using Conan Doyle's autobiographical, scientific, and literary primary sources, as well as past and current medical literature. The Adventure of the Missing Three-Quarter was autobiographical. Conan Doyle himself was a rugby player and his wife died of tuberculosis. Furthermore, in 1884, in The Lancet, he described the hereditary case of a female gouty patient, presenting with ocular manifestations. In agreement with the concept of rich man's gout, the gouty patient of Sherlock Holmes' story, Lord Mount James, was a rich irascible noble but he was not addicted to the pleasures of food and sex. Following the usual funny representation of gouty patients, Conan Doyle made fun of Lord Mount James, but he misquoted a true case of gout cited in the literature. In his scientific and literary production on gout, Conan Doyle stuck to the most updated medical concepts of the time, demonstrating an uncommon knowledge of scientific literature.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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 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".