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Record W1980877337 · doi:10.1155/2014/465932

Poor Reporting of Outcomes Beyond Accuracy in Point-of-Care Tests for Syphilis: A Call for a Framework

2014· review· en· W1980877337 on OpenAlexafffund
Yalda Jafari, Mira Johri, Lawrence Joseph, Caroline Vadnais, Nitika Pant Pai

Bibliographic record

VenueAIDS Research and Treatment · 2014
Typereview
Languageen
FieldMedicine
TopicSyphilis Diagnosis and Treatment
Canadian institutionsRoyal Victoria HospitalMcGill University Health CentreRoyal Victoria Regional Health CentreUniversité de MontréalCentre Hospitalier de l’Université de MontréalMcGill University
FundersCanadian Institutes of Health Research
KeywordsSerostatusMedicineSyphilisDocumentationCLARITYPoint-of-care testingPoint of careFamily medicineComputer scienceNursingHuman immunodeficiency virus (HIV)Pathology

Abstract

fetched live from OpenAlex

Background. Point-of-care (POC) diagnostics for syphilis can contribute to epidemic control by offering a timely knowledge of serostatus. Although accuracy data on POC syphilis tests have been widely published, few studies have evaluated broader outcomes beyond accuracy that impact patients and health systems. We comprehensively reviewed evidence and reporting of these implementation research outcomes (IROs), and proposed a framework to improve their quality. Methods. Three reviewers systematically searched 6 electronic databases from 1980 to 2014 for syphilis POC studies reporting IROs. Data were abstracted and findings synthesised narratively. Results. Of 71 studies identified, 38 documented IROs. IROs were subclassified into preference (7), acceptability (15), feasibility (15), barriers and challenges (15), impact (13), and prevalence (23). Using our framework and definitions, a pattern of incomplete documentation, inconsistent definitions, and lack of clarity was identified across all IROs. Conclusion. Although POC screening tests for syphilis were generally favourably evaluated across a range of outcomes, the quality of evidence was compromised by inconsistent definitions, poor methodology, and documentation of outcomes. A framework for standardized reporting of outcomes beyond accuracy was proposed and considered a necessary first step towards an effective implementation of these metrics in POC diagnostics research.

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)0.001
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.000
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 head0.234
GPT teacher head0.527
Teacher spread0.293 · 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 designOther design
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

Citations7
Published2014
Admission routes2
Has abstractyes

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