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Record W3021293999 · doi:10.1136/sextrans-2019-sti.637

P563 MSM predictive modeling within a large, linked database of laboratory, surveillance, and administrative healthcare records

2019· article· en· W3021293999 on OpenAlexaff
Travis Salway, Zahid A Butt, Stanley Wong, Carmine Rossi, Jason Wong, Amanda Yu, Maria Alvarez, Troy Grennan, Mark Gilbert, Mel Krajden, Naveed Z. Janjua

Bibliographic record

VenuePoster presentations · 2019
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsBC Centre for Disease Control
Fundersnot available
KeywordsMen who have sex with menMedicineSyphilisDatabaseCohortGonorrheaDemographyFamily medicineInternal medicineHuman immunodeficiency virus (HIV)Computer science

Abstract

fetched live from OpenAlex

<h3>Background</h3> Enumeration or measurement of populations of men who have sex with men (MSM) is critical to developing and evaluating sexually transmitted and bloodborne infection (STBBI) prevention and treatment programs. However, there is a lack of data sources in which sexual orientation or behaviour is measured. In this study, we present the development and validation of a novel model (i.e., ‘computational phenotype’) to predict MSM status using multiple data sources. <h3>Methods</h3> Three disease case surveillance databases (HIV, hepatitis B and C, and syphilis), a public health laboratory database (which performs ≥95% of all HIV, hepatitis C and syphilis tests in British Columbia), and five administrative health record databases were linked and aggregated, resulting in a retrospective cohort of 727,091 adult men from 1990 to 2013. Self-reported MSM status (‘gold-standard’) from the three disease case surveillance databases was used to develop a multivariable prediction model for identifying MSM in the larger cohort. Models were selected using ‘elastic-net’ (combination of lasso and ridge regression), implemented through the GLMNet package in R, and a final model optimized area under the receiver operating characteristics curve (AUC). <h3>Results</h3> History of gonorrhea and syphilis diagnoses, HIV tests in the past year, history of visit to identified gay and bisexual men’s clinics, and residence in MSM-dense neighborhoods (based on self-reported MSM) were all positively associated with being MSM. The selected model had a sensitivity of 72%, specificity of 94%, and AUC of 92%. Combining self-reported MSM (n=6,280) and predicted MSM (n=85,521), a total of 91,801 men (13% of the cohort) were classified as MSM. <h3>Conclusion</h3> Applying a computational phenotyping method to administrative data yielded a cohort of 85,521 MSM, which may be used to monitor and evaluate health outcomes and healthcare utilization. Sensitivity and specificity of this model were comparable to interviewer-administered self-report measures of sexual orientation. <h3>Disclosure</h3> No significant relationships.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.649

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)0.0000.000

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 head0.074
GPT teacher head0.395
Teacher spread0.321 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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