Bilharzia in the Philippines: past, present, and future
Bibliographic record
Abstract
Schistosomiasis japonica has a long history in the Philippines. In 1975, 24 endemic provinces were identified in the northern, central, and southern islands of the Philippines. More than five million people were at risk, with approximately one million infected. In 2003, new foci of infection were found in two provinces in the north and central areas. For the past 30 years, human mass drug administration (MDA), utilizing the drug praziquantel, has been the mainstay of control in the country. Recent studies have shown that the schistosomiasis prevalence ranges from 1% to 50% within different endemic zones. Severe end-organ morbidity is still present in many endemic areas, particularly in remote villages with poor treatment coverage. Moreover, subtle morbidities such as growth retardation, malnutrition, anemia, and poor cognitive function in infected children persist. There is now strong evidence that large mammals (e.g. water buffaloes, cattle) contribute significantly to disease transmission, complicating control efforts. Given the zoonotic nature of schistosomiasis in the Philippines, it is evident that the incidence, prevalence, and morbidity of the disease will not be controlled by MDA alone. There is a need for innovative cost-effective strategies to control schistosomiasis in the long term.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".