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Record W2314572856 · doi:10.11622/smedj.2014025

Dr Wu Lien-teh: modernising post-1911 China’s public health service

2014· article· en· W2314572856 on OpenAlexaff
TK Wong, T M Ho, Ng Kh

Bibliographic record

VenueSingapore Medical Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicTravel-related health issues
Canadian institutionsInnovation Cluster (Canada)
Fundersnot available
KeywordsChinaBattlePlague (disease)QuarantineMedicineAncient historyPublic healthHarmEconomic historyHistoryLawPolitical science

Abstract

fetched live from OpenAlex

A young Chinese doctor from a small British colony gaining fame for his role in ending a pneumonic plague in faraway north-east China is indeed a remarkable story. Wu Lien-teh (1879–1960), although standing at only 5 feet 2 inches, short even by Chinese standards, towered over many of his contemporaries because of his dedicated medical work. He was also prominent in the advancement of social and cultural causes. In particular, he campaigned against the opium trade, which had caused irreparable harm to health in China and Southeast Asia. Beyond his battle against the pneumonic plague in Manchuria, Wu was also in the forefront of efforts to create a modern public health service in China. His efforts helped China regain control of quarantine centres in all major ports that had come under the supervision of foreign powers. Wu was also called to deal with the cholera epidemic in China's north-east region in 1920–21. Active in international conferences and research, Wu was the first Chinese to have his work published in the prestigious medical journal, Lancet. For his contributions, Wu was conferred honorary doctorates by Peking University, Hong Kong University and Tokyo University. In 1935, he was nominated for the Nobel prize for his fight against the 1910 Manchurian plague and for identifying the role of tarbagan marmots in the transmission of the disease.(1) The epidemic, one of the deadliest of its kind, killed an estimated 60,000 people in the affected regions of Manchuria during the seven months that it lasted.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.004
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 head0.050
GPT teacher head0.347
Teacher spread0.297 · 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 designOther design
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

Citations10
Published2014
Admission routes1
Has abstractyes

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