Metal phosphide poisoning in a disaster‐stricken area. Can early hemodialysis improve outcomes?
Bibliographic record
Abstract
BACKGROUND: Phosphide metal poisoning results in tens of thousands of fatalities per year worldwide. The mortality in critically ill patients often exceeds 50%. The available treatment is supportive and there is no antidote. Dialysis is recommended to treat advanced complications but has not been prescribed early in the process. In this study we report our experience in using dialysis in the early hours of presentation of the patients and suggest it can favorably improve the prognosis. We also draw attention to the risk of suicide under conditions of chronic conflict such as those in northwestern Syria, and to the lack of necessary mental health support for patients after suicide attempts. METHODS: Retrospective review of records of patients poisoned with aluminum phosphide and admitted to critical care facilities in northwestern Syria between July 2022 and June 2023. RESULTS: During the observation period 16 cases were encountered. Suicide was the reason of the poisoning in 15 patients, the median patient age was 18 years and over two thirds of the patients were female. Early dialysis was used in 11 patients who were critically ill and their mortality rate was 18%. CONCLUSIONS: Phosphide metal poisoning is common in the disasters stricken area of northwestern Syria. Most cases are suicidal and impact young females. Early dialytic interventions may favorably impact the outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".