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Record W3017708929 · doi:10.2174/1874944502013010134

Systematic Literature Review on strengthening Eye Care Follow-Up Among Glaucoma Patients in Limpopo Province

2020· article· en· W3017708929 on OpenAlexaboutno aff
Shonisani Tshivhase

Bibliographic record

VenueThe Open Public Health Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsnot available
Fundersnot available
KeywordsJadad scaleMedicineObservational studyRandomized controlled trialGlaucomaPsychological interventionPhysical therapyMEDLINESystematic reviewFamily medicinePediatricsOptometryInternal medicineOphthalmologyNursing

Abstract

fetched live from OpenAlex

Background: Adherence to prescribed glaucoma medications is often poor and proper adherence can be challenging for most patients Objective: The purpose of this systematic literature review is to identify and evaluate studies that have tested the impact of each intervention on glaucoma adherence based on their quality outcome measure. Methods: A comprehensive search of database was conducted from January 2009 to January 2019. We systematically reviewed the literature and identified sixteen studies that used educational interventions to improve glaucoma medication adherence. Eleven out of 16 eligible studies were subjected to Randomized Controlled Trial (RCTs) and the remaining four were reviewed as observational studies. One study was reviewed through both observational plus randomized control trial method. Results: Out of the eleven (68.75%) RCTs interventions done, five (31.25%) showed improvement in medication adherence and persistence with eye drop instillation, whereas (n=6) did not show any significant improvement on their medication adherence. The quality of each study was evaluated using the Jadad score calculation and the Ottawa-Newcastle. Conclusion: Using information from this systematic review and Health Behavior Model, we created a theoretical framework to illustrate how counseling and education can improve medication adherence amongst glaucoma patients in the country.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.247
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.311
Teacher spread0.284 · 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
Published2020
Admission routes1
Has abstractyes

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