MétaCan
Menu
Back to cohort
Record W2079245179 · doi:10.1002/asi.22737

Modeling the relationship between an emerging infectious disease epidemic and the body of scientific literature associated with it: The case of <scp>HIV</scp>/<scp>AIDS</scp> in the <scp>U</scp>nited <scp>S</scp>tates

2012· article· en· W2079245179 on OpenAlexfundno aff
Jeff Naidoo, Jeffrey T. Huber, Pamela K. Cupp, Qishan Wu

Bibliographic record

VenueJournal of the American Society for Information Science and Technology · 2012
Typearticle
Languageen
FieldMedicine
TopicData-Driven Disease Surveillance
Canadian institutionsnot available
FundersU.S. National Library of MedicineCenters for Disease Control and PreventionPublic Health Agency of Canada
KeywordsScientific literatureHuman immunodeficiency virus (HIV)Public healthDiseaseMedicineDemographyGerontologyFamily medicineInternal medicineSociologyPathologyBiology

Abstract

fetched live from OpenAlex

This study undertook an exploratory analysis of the relationship between the body of scientific literature associated with HIV/AIDS and the trajectory of the epidemic, measured by the rate of new cases diagnosed annually in the United States for the period covering 1981 to 2009. The body of scientific literature examined in this investigation was constituted from scientific research that developed alongside the epidemic and was extracted from MEDLINE, a bibliographic database of the United States. National Library of Medicine. Content analysis methods were employed for qualitative data reduction, and regression analysis was used to assess whether variation in the trajectory of the epidemic co‐occurred with variation in the publication of specific genres of content within the scientific literature relating to HIV/AIDS. The regression model confirmed a statistically significant association between the representative body of HIV/AIDS scientific literature and the epidemic trajectory, and identified three research categories, namely, ameliorative drug treatments, other clinical protocols, and health education, as being most significantly associated with the epidemic trajectory. Implicit in the findings of this study are areas of scientific research that are of functional and practical interest to clinicians, policy makers, the lay public, and contributors to the body of scientific literature.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Simulation or modelinglow
gptBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Simulation or modelinglow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.013
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.992
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.054
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.009
Science and technology studies0.0010.002
Scholarly communication0.0060.004
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.305
Teacher spread0.282 · 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

Labeled directly by 2 models reading the full record.

Study designSimulation or modeling
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
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Society for Information Science and TechnologySame topicData-Driven Disease SurveillanceCategoryBibliometricsFrench-language works237,207