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Record W2133122432 · doi:10.1086/319234

Internet and Computer-Based Resources for Travel Medicine Practitioners

2001· article· en· W2133122432 on OpenAlexaff
Victor L. Yu, Jay S. Keystone, Phyllis E. Kozarsky, David O. Freedman

Bibliographic record

VenueClinical Infectious Diseases · 2001
Typearticle
Languageen
FieldMedicine
TopicTravel-related health issues
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsTravel medicinePublic healthThe InternetMedicineInfectious disease (medical specialty)EpidemiologyGovernment (linguistics)MEDLINEDiseaseEnvironmental healthGlobal healthWeb sitePublic relationsFamily medicineWorld Wide WebPathologyPolitical science

Abstract

fetched live from OpenAlex

The field of travel medicine is concerned primarily with ways to maintain the health of international travelers. Remaining current in this area requires up-to-date knowledge of global infectious diseases, patterns of drug resistance, advances in preventive measures, and public health regulations pertaining to the crossing of international borders. This review of off-line commercial databases and Internet sources will assist infectious disease consultants in accessing current reliable travel health information. Of the North American pretravel off-line databases, TRAVAX (United States) and The Medical Letter are the most comprehensive, whereas the Global Infectious Disease and Epidemiology Network is extraordinary in its provision of global infectious disease epidemiology for posttravel assessment. A total of 65 Web sites are listed within 9 categories, covering such areas as authoritative government travel health recommendations, commercially-oriented sites, and travel medicine listserv discussion groups. Even among reputable Web sites, contradictory information may be found that demands careful consideration by the clinician and by the traveling public.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.030
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.083
GPT teacher head0.415
Teacher spread0.332 · 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

Citations37
Published2001
Admission routes1
Has abstractyes

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