MétaCan
Menu
Back to cohort
Record W2794982962 · doi:10.2196/10591

Measles Outbreak-Khywan, Huth District, Amran Governorate, Yemen, March, 2017

2018· article· en· W2794982962 on OpenAlexvenueno aff
Mona Mayad, W Albakhshi

Bibliographic record

VenueIproceedings · 2018
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Vectors
Canadian institutionsnot available
Fundersnot available
KeywordsOutbreakRashMedicineMeaslesEnvironmental healthGeographyMedical emergencyVirologyVaccinationDermatology

Abstract

fetched live from OpenAlex

Background: On March 3, 2017, cases with fever and skin rash were reported in Khywan, Huth district, Amran by electronic disease early warning system (eDEWS) coordinator. A team from Y-FETP was sent to investigate the problem. Objective: To confirm the existence of outbreak and recommend control measures. Methods: We conducted active house to house- case search. Suspected case was any person with fever and skin rash at any age in Khywan area, Huth district, Amran governorate, from 5 February to 5 March 2017. Data analysis was performed by Epi Info version 7.2. Results: Results 32 cases met the standard case definition, the index case was reported in 5 Feb. 2017, the outbreak continued till 5 March with the peak of cases in 3 March 2017 (6 cases). The percentage of females was 53%. The highest percent was among the age group 1-<5 years with the attack rate 21/1000 of population. The percent of non-vaccinated cases was 97%. Reasons for non-vaccination were: lack of outreach vaccination services (39%), absence of vaccine in the nearby health center (39%), and vaccination refusal (22%). All cases had fever and skin rash, 97% of cases had cough and conjunctivitis. About 13% of cases were admitted in Huth hospital due to pneumoniae as a complication of measles. Conclusions: Existence of Measles outbreak in Khywan area, Huth district, Amran governorate was confirmed. Urgent measles immunization campaign with ongoing routine and outreach immunization services are recommended to increase vaccination coverage.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.001

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 head0.034
GPT teacher head0.301
Teacher spread0.267 · 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 designNot applicable
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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueIproceedingsSame topicViral Infections and VectorsFrench-language works237,207