Capacity to Treat Retinoblastoma in the Philippines
Bibliographic record
Abstract
Objective: This study determined the capacity to treat retinoblastoma (RB) in the Philippines. Method: This was a cross-sectional study which included all Department of Health (DOH) hospitals and all government and private tertiary hospitals in the Philippines. An online modified questionnaire taken from the St. Jude Children’s Research Hospital study on RB assessment was emailed to 143 hospitals in all 17 regions. Primary outcome measures were human resources capacity, treatment capacity and education and network capacity. Availability, frequency and confidence of use were assessed for treatment capacity. An asset-based tier classification of hospitals was created based on the human resources capacity and treatment capacity. Results: This assessment survey had a 49% response rate. There were 3 regions that had zero correspondence. A general ophthalmologist, a subspecialized ophthalmologist, and a general pathologist were the most available physicians in the management of RB. Almost all respondents had the necessary diagnostic technology in their hospitals, but frequency and confidence of use were low. A quarter of the respondents (23%) used a standardized treatment protocol, while only 26% had established a referral network in their area. Tier classification was able to differentiate capacities for particular resources only. Only 23% of the participating hospitals have the capacity to treat RB through its trained human resources, available diagnostic and treatment technology, and education and network programs. Distribution of hospitals identified as tier I, II and III were 18%, 8%, and 41%, respectively. Conclusion: Capacity to treat RB in the Philippines is evident only in a few hospitals, majority of which are located in the National Capital Region. However, tier 3 hospitals were identified in 10 other regions, making accessibility to RB care possible to patients in the provinces. Although human resource and technology are made available, utilization of these resources is low in many hospitals for the management of RB. Clinical practice guidelines for RB is still lacking. Connecting the different tiers in each region as a form of referral network can improve capacity and management of RB.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".