Referral patterns for retinoblastoma patients in Ethiopia
Bibliographic record
Abstract
BACKGROUND: Increased lag time between the onset of symptoms and treatment of retinoblastoma (RB) is one of the factors contributing to delay in diagnosis. The aim of this study was to understand the referral patterns and lag times for RB patients who were treated at Menelik II Hospital in Addis Ababa, Ethiopia. METHOD: A single-center, cross- sectional study was conducted in January 2018. All new patients with a confirmed RB diagnosis who had presented to Menelik II Hospital from May 2015 to May 2017 were eligible. A questionnaire developed by the research team was administered to the patient's caregiver by phone. RESULTS: Thirty-eight patients were included in the study and completed the phone survey. Twenty-nine patients (76.3%) delayed seeing a health care provider for ≥ 3 months from the onset of symptoms, with the most common reason being the belief that it was not a problem (96.5%), followed by 73% saying it was too expensive. The majority of patients (37/38, 97.4%) visited at least 1 additional health care facility prior to reaching a RB treatment facility. The mean overall lag time from noticing the first symptom to treatment was 14.31 (range 0.25-62.25) months. CONCLUSION: Lack of knowledge and cost are major barriers to patients first seeking care for RB symptoms. Cost and travel distance are major barriers to seeing referred providers and receiving definitive treatment. Delays in care may be alleviated by public education, early screening, and public assistance programs.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".