Clinical features, treatment, prognosis, and mortality in paraquat poisonings: A hospital-based study in Iran
Bibliographic record
Abstract
OBJECTIVE: The aim of the present study was to evaluate the demographics, clinical characteristics, fatal dose, the efficacy of treatments, and prognosis in paraquat (PQ) poisoning in the Kerman Province of Iran. METHODS: This analytical cross-sectional study was conducted on 126 PQ poisoned patients who were referred to Afzalipour Hospital during 2006-2015. Demographic variables such as age and gender, signs and symptoms of poisoning, the estimated ingested dosage of PQ, and clinical outcome were extracted from medical records. Patients were compared and categorized into two groups considering the outcome: survivors and nonsurvivors. Patients with nonoral exposures, combined drug exposures, PQ exposures more than 24 h before the presentation, and critical underlying diseases were not included in the study. FINDINGS: Our results indicated that the mean dose of PQ used by all patients was 2358 mg, which was reported as 1846 and 2812 mg in females and males, respectively. Moreover, the results showed that the highest mortality rate was in patients with respiratory distress, followed by oral ulceration and excess salivation. In all PQ-poisoned patients, the dose of greater than approximately 2250 mg predicted death with 86.2% specificity and 75.7% sensitivity. CONCLUSION: Based on the results of the present study, the mortality rate in PQ-poisoned patients depended on the dose of poison, blood sugar level, and aspartate transaminase levels. Our results suggest that these parameters have excellent prognostic value for the prediction of mortality.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".