The National Poisons Information Centre in Sri Lanka: The First Ten Years
Bibliographic record
Abstract
INTRODUCTION: Poisoning is a major health concern in Sri Lanka, which has a very high morbidity and mortality from pesticide poisoning. Poisoning, which continues to be in the first five leading causes of death, accounts for about 80,000 hospitalizations and over 3,000 deaths per year. The National Poisons Information Centre in Sri Lanka, thefirst such centre to be established in South Asia, completed 10 years service in 1997. The 4,070 calls received in thefirst 10 years are analyzed and compared with the national hospitalization pattern. METHODS: The recorded data sheets of all enquiries received from 1988 to 1997 were analyzed retrospectively to study (1) purpose of enquiry, (2) category of enquirer, (3) circumstances of poisoning, (4) gender of victim, (5) age of victim, (6) type of poison, and (7) outcome. Items (6) and (7) were compared with the national hospital statistics for 1998. RESULTS: Of the 4,070 enquiries, 92% concerned specific patients and 6% were for information on poisons. Almost 90% of the enquiries were from medical or paramedical personnel, 5% from relatives or friends, and 3% from the victims. Nearly 38% of enquiries concerned pesticides compared to 27% of poisoning hospitalizations. Medicinal agents were the subject of 20% of enquiries compared to 13% of hospitalizations. The major discrepancy was for snake bites, accounting for only 6% of enquiries but 42% of hospitalizations. Sex distribution of enquiries showed more males than females. Thirty-seven percent of the victims were young adults-15-29years age group. Nearly 49% of the enquiries were for suicidal attempts. Seventy-one percent of the victims recovered. CONCLUSIONS: Although enquiries to the NPIC averaged only 0.5% of poisoning hospitalizations, they were sufficiently representative of the national pattern to predict that increasing utilization of the NPIC would offer a much needed service, both for
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".