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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".