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Record W4400955154 · doi:10.3233/shti240182

Acceptability of Home Monitoring for Neovascular Age-Related Macular Degeneration Reactivation: A Qualitative Study

2024· article· en· W4400955154 on OpenAlexafffund
Seán O’Connor, Charlene Treanor, Elizabeth C. Ward, Robin Wickens, Abby O’Connell, Lucy Culliford, Chris Rogers, Eleanor Gidman, Tünde Pető, Paul C. Knox, Ben Burton, Andrew Lotery, Sobha Sivaprasad, Barnaby C Reeves, Ruth Hogg, Michael Donnelly

Bibliographic record

VenueStudies in health technology and informatics · 2024
Typearticle
Languageen
FieldMedicine
TopicRetinal Imaging and Analysis
Canadian institutionsQueen's University
FundersQueen's UniversityNational Institute for Health and Care ResearchUnited Kingdom Clinical Research Collaboration
KeywordsMacular degenerationThematic analysisMedicineQualitative researchHealth professionalsTest (biology)Health careOphthalmology

Abstract

fetched live from OpenAlex

This study formed part of a diagnostic test accuracy study to quantify the ability of three index home monitoring (HM) tests (one paper-based and two digital tests) to identify reactivation in Neovascular age-related macular degeneration (nAMD). The aim of the study was to investigate views about acceptability and explore adherence to weekly HM. Semi-structured interviews were held with 98 patients, family members, and healthcare professionals. A thematic approach was used which was informed by theories of technology acceptance. Various factors influenced acceptability including a patient's understanding about the purpose of monitoring. Training and ongoing support were regarded as essential for overcoming unfamiliarity with digital technology. Findings have implications for implementation of digital HM in the care of older people with nAMD and other long-term conditions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.075
GPT teacher head0.465
Teacher spread0.390 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2024
Admission routes2
Has abstractyes

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