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Record W1600099392 · doi:10.3138/cbmh.27.2.343

Lost in Transition: Influenza in the British Army in the 1830s and 1840s

2010· article· en· W1600099392 on OpenAlexafffundvenue
Janet Padiak, D. Ann Herring

Bibliographic record

VenueCanadian Journal of Health History · 2010
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsMcMaster University
FundersSocial Sciences and Humanities Research Council of CanadaArts Research Board, McMaster University
KeywordsHistoryMedicineEpidemiologyCausationInfluenza pandemicDiseaseFamily medicineDemographyCoronavirus disease 2019 (COVID-19)PathologyInfectious disease (medical specialty)Political scienceSociologyLaw

Abstract

fetched live from OpenAlex

This paper uses surgeons' reports from the 1830s and 1840s to investigate routine regimental medical care by focusing on a familiar, non-fatal disease. The regimental reports are used to describe the classification of influenza and the use of antiphlogistic regimen to treat the disease. Also discussed is how the surgeons reconciled the rapid spread of influenza with the predominant causation beliefs of the time. Furthermore, the patterns of influenza morbidity in the early middle 19th century are discussed, adding to the understanding of the historical epidemiology of this genetically variable virus.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score0.910

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.075
GPT teacher head0.341
Teacher spread0.266 · 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 designObservational
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

Citations2
Published2010
Admission routes3
Has abstractyes

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