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Record W2296430159 · doi:10.1017/s0950268816000479

Enhanced risk of illness during the 1918 influenza pandemic after previous influenza-like illnesses in three military populations

2016· article· en· W2296430159 on OpenAlexaboutno aff
G. Dennis Shanks, Steven Burroughs, Joshua D. Sohn, Norman C. Waters, Virginia F. Smith, Michael Waller, John F. Brundage

Bibliographic record

VenueEpidemiology and Infection · 2016
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsnot available
FundersU.S. Military AcademyU.S. Naval AcademyU.S. Department of Defense
KeywordsPandemicMedicineHuman mortality from H5N1OfficerInfluenza pandemicRelative riskConfidence intervalInfluenza A virusVirusDemographyImmunologyFamily medicineInfectious disease (medical specialty)Coronavirus disease 2019 (COVID-19)Internal medicineDiseaseHistory

Abstract

fetched live from OpenAlex

The reasons for the unprecedented mortality during the 1918 influenza pandemic remain poorly understood. We examined morbidity records from three military cohorts from years prior to and during the 1918 pandemic period to assess the effects of previous respiratory illnesses on experiences during the pandemic. Clinical registers and morbidity lists were examined to identify all medical encounters for acute respiratory illnesses in students at two U.S. military officer training academies and Australian soldiers deployed in Europe. Influenza-like illness prior to the major pandemic wave of 1918 predisposed Australian soldiers [relative risk (RR) 1·37, 95% confidence interval (CI) 1·18-1·60, P < 0·0001] and US officer trainees at West Point (RR 3·10, 95% CI 2·13-4·52, P < 0·0001) and Annapolis (RR 2·03, 95% CI 1·65-2·50, P < 0·0001) to increased risks of medically treated illnesses in late 1918. The findings suggest that susceptibility to and/or clinical expressions of the 1918 pandemic influenza virus depended on previous experiences with respiratory infectious agents. The findings are consistent with observations during the 2009 pandemic in Canada and may reflect antibody-dependent enhancement of influenza infection.

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)0.002
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.076
GPT teacher head0.383
Teacher spread0.307 · 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.

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

Citations7
Published2016
Admission routes1
Has abstractyes

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