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Record W1577032408 · doi:10.3138/cbmh.25.2.515

Dominique-Jean Larrey, Chief Surgeon of the French Army with Napoleon in Egypt: Notes and Observations on Larrey’s Medical Memoirs Based on the Egyptian Campaign

2008· article· en· W1577032408 on OpenAlexvenueno aff
Mary Mendenhall Wood

Bibliographic record

VenueCanadian Journal of Health History · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Medicine Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMemoirMedicineAncient historyClassicsSurgeryHistoryArt history

Abstract

fetched live from OpenAlex

Dr. Dominique-Jean Larrey wrote memoirs of 12 diseases he encountered while serving as chief surgeon of the French army during Napoleon's Egyptian campaign. He describes symptoms and treatments, evaluates remedies used by the Egyptians, and the effects of the climate. Of interest are his original though misguided explanations of causes of sickness or complications from wounds as well as descriptions of medications--now known to be dangerous--but all balanced by his common sense and efforts to ameliorate suffering.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.099
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.004
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.062
GPT teacher head0.219
Teacher spread0.157 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2008
Admission routes1
Has abstractyes

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