Retinoblastoma referral pattern in kenya
Bibliographic record
Abstract
PURPOSE: Kenya is a large country with a widely dispersed population. As retinoblastoma requires specialized treatment, we determined the referral pattern for patients with retinoblastoma in Kenya to facilitate the formulation of a national policy. MATERIALS AND METHODS: A retrospective study was performed for retinoblastoma patients who presented from January 1, 2006 to December 31, 2007. Data were collected on the referral process from presenting health facility to the hospital where patient was treated. Data were also collected on the time interval when the first symptoms were noticed to the time of presentation at a health facility (lag time). For cases that could be traced to a referral hospital, the time delay due to referral (referral lag time) was recorded. RESULTS: There were 206 patients diagnosed with retinoblastoma in 51 Kenyan and 2 foreign healthcare facilities, and they received final treatment at a Kenyan hospital. Mean lag time was 6.8 months (±6.45). Of all patients, 18% (38/206) were treated at the hospital where they first presented and 82% (168/206) were referred elsewhere. Of those referred, 35% (58/168) were lost to follow-up. The mean referral lag time was 1.7 months (±2.5). CONCLUSIONS: A significant proportion of cases presented late, and either delayed seeking further treatment or were lost after initial referral. We recommend the implementation of a national strategy that emphasizes early detection, documentation and follow up of retinoblastoma patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".