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Record W7132947402

The Development of Patient-Reported Outcome Measures and an Observer-Reported Outcome Measure for Pediatric Patients with Inherited Retinal Disease

2023· dissertation· W7132947402 on OpenAlexaff
Kavin Selvan

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsUniversity of Toronto
FundersUniversity of Michigan
KeywordsPsychosocialThematic analysisOutcome (game theory)Focus groupQualitative researchPatient-reported outcome
DOInot available

Abstract

fetched live from OpenAlex

Objective: This study aimed to devise valid patient-reported and observer-reported outcome measures for inherited retinal diseases (IRDs) in pediatric patients and caregivers. Methods: Guided by a literature review and IRD specialist focus groups, measures were developed for patients aged 6 to <13 years. Interviews were conducted with 26 children and 23 caregivers, using thematic analysis and qualitative description methdology. Existing adult measures were validated in 91 adolescents (13 to <18 years) with IRDs. Results: Children and their caregivers each identified 16 themes focused on the impact of their condition on daily life and psychosocial health. The adult measures were validated for use in adolescents. Conclusion: The study underlines the need for tailored outcome measures addressing the experiences of adolescents, children with IRDs, and caregivers. These tailored tools offer valuable resources for eye care providers to optimize the management of IRDs in pediatric patients, including both children and adolescents.

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)0.057
metaresearch head score (Gemma)0.102
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.057
Threshold uncertainty score0.303

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0570.102
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.118
GPT teacher head0.410
Teacher spread0.291 · 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 designObservational
Domainnot available
GenreMethods

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
Published2023
Admission routes1
Has abstractyes

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