Contrast sensitivity and visual hallucinations in patients referred to a low vision rehabilitation clinic
Bibliographic record
Abstract
Objective Mental health problems are prevalent in youth with rheumatologic disease. Gaps in knowledge exist regarding their effect, as well as strategies for detection and effective treatment. To address these gaps, the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Mental Health Workgroup developed and prioritized an agenda of research topics. Methods We systematically reviewed the literature and identified 5 major research domains in further need of study: (A) mental health burden and relationship to pediatric rheumatologic disease, (B) effect of mental health disorders on outcomes, (C) mental health awareness and education, (D) mental health screening, and (E) mental health treatment. Research topics within these areas were developed by workgroup leaders and refined by the workgroup. Members were surveyed to prioritize the topics by importance, feasibility of study, and actionability. Results Fifty-nine members (57%) completed the survey. Among the proposed research topics, 31/33 were rated as highly important and 4/33 were rated highly for importance, feasibility, and actionability. Topics rated most important related to (A) mental health burden and relationship to rheumatologic disease, and (B) the effect of mental health on outcomes. Topics rated most feasible and actionable were related to (D) mental health screening. Conclusion Addressing gaps in knowledge regarding mental health in youth with rheumatologic disease is essential for improving care. We have identified high priority research topics regarding mental health of pediatric rheumatology patients in need of further investigation that are feasible to study and believed to lead to actionable results in patient care.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".