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Record W2104860068 · doi:10.1017/s0950268805005133

Challenges in identifying the methodology to estimate the prevalence of infectious intestinal disease in Malta

2005· article· en· W2104860068 on OpenAlexfundno aff
Charmaine Gauci, H. M. Gilles, S. O’Brien, Julian Mamo, Isabel Stabile, Franco Maria Ruggeri, C. Micallef

Bibliographic record

VenueEpidemiology and Infection · 2005
Typearticle
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsnot available
FundersHealth CanadaCenters for Disease Control and Prevention
KeywordsSelection biasStratified samplingRecall biasMedicineSample size determinationStatisticsEpidemiologyPopulationSampling biasSampling (signal processing)Sample (material)DemographyEnvironmental healthComputer scienceMathematicsPathology

Abstract

fetched live from OpenAlex

Routine surveillance systems capture only a fraction of infectious intestinal disease (IID) that is actually occurring in the community. Different methodologies utilized among various international studies in the field were reviewed in order to devise an appropriate survey to obtain current estimates of prevalence of IID in Malta. An age-stratified retrospective cross-sectional telephone study was selected for the study due to its feasibility in terms of limited resources necessary (funds, time and human). The disadvantages of this type of study include the inherent biases such as selection bias (sampling, ascertainment and participation bias) and information bias (recall and observer bias). A pilot study was carried out using a random age-stratified sample of 100 persons over a 3-month period. A total of 5.0% (95% CI +/-4.27) of the population was estimated to have suffered from IID during that period. This estimate was used in order to assist in sample size calculations for a large-scale community study. It also served to test the survey instrument and methodology and to identify operational problems.

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)0.008
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.020
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)0.0000.000

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 head0.221
GPT teacher head0.473
Teacher spread0.252 · 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.

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

Citations11
Published2005
Admission routes1
Has abstractyes

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