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Record W2893678736 · doi:10.12809/hkmj187470

Measles: a disease often forgotten but not gone

2018· review· en· W2893678736 on OpenAlexaff
Alexander K. C. Leung, KL Hon, KF Leong, Consolato Sergi

Bibliographic record

VenueHong Kong Medical Journal · 2018
Typereview
Languageen
FieldMedicine
TopicVirology and Viral Diseases
Canadian institutionsUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMeaslesMeasles virusMedicineVirologyMaculopapular rashRashMorbillivirusMeasles vaccineVaccinationImmunologyVirusDermatology

Abstract

fetched live from OpenAlex

family. The illness typically begins with fever, runny nose, cough, and pathognomonic enanthem (Koplik spots) followed by a characteristic erythematous, maculopapular rash. The rash classically begins on the face and becomes more confluent as it spreads cephalocaudally. Laboratory confirmation of measles virus infection can be based on a positive serological test for measles-specific immunoglobulin M antibody, a four-fold or greater increase in measles-specific immunoglobulin G between acute and convalescent sera, isolation of measles virus in culture, or detection of measles virus ribonucleic acid by reverse transcriptase-polymerase chain reaction. Complications occur in 10% to 40% of patients, and treatment is mainly symptomatic. Bacterial superinfections, if present, should be properly treated with antibiotics. To eradicate measles, universal childhood immunisation and vaccination of all susceptible individuals with measles vaccine would be ideal. In developed countries, routine immunisation with measles-containing vaccine is recommended, with the first and second doses at ages 12 to 15 months and 4 to 6 years, respectively. The World Health Organization recommends that the first and second doses of measles-containing vaccine be given at ages 9 months and 15 to 18 months, respectively, in countries with high rates of measles transmission.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.072
GPT teacher head0.380
Teacher spread0.308 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations81
Published2018
Admission routes1
Has abstractyes

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