MétaCan
Menu
Back to cohort
Record W4297858085 · doi:10.5206/uwomj.v90i1.14004

The Independent Inventions of General Anesthesia in 19th century Japan and United States

2022· article· en· W4297858085 on OpenAlexvenueno aff
Arjun Patel

Bibliographic record

VenueUniversity of Western Ontario Medical Journal · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicMedical History and Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)General anestheticsAnestheticNitrous oxideMedicineEther anesthesiaAnesthesiaHistoryArchaeology

Abstract

fetched live from OpenAlex

In October of 1804, surgeon Hanaoka Seishū performed the first documented surgery using general anesthesia. Hanaoka’s formulation, a mixture of plant extracts called tsūsensan, was commonly used in Japan, but it had minimal influence elsewhere. It was not until the 1840s that general anesthesia was used outside of Japan. In the eastern United States, four individuals, including dentist William Morton, were independently experimenting with the use of diethyl ether and nitrous oxide as general anesthetics. Unlike Hanaoka’s invention, Morton’s successful use of diethyl ether in a neck tumour removal surgery sparked the rapid development and proliferation of new anesthetic technologies and compounds throughout the world. This paper examines why Hanaoka’s work was not significantly influential outside of Japan, while the later American discoveries achieved global prominence despite being four decades later. Additionally, historical context behind both stories is included. Reasons for this disparity include Japan’s isolationist policy at the time (sakoku) and differences in 19th century Western and Japanese approaches to medical education and research.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.927
Threshold uncertainty score0.996

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.018
GPT teacher head0.190
Teacher spread0.172 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueUniversity of Western Ontario Medical JournalSame topicMedical History and InnovationsFrench-language works237,207