Tropical Cyclone Gonu: Number of Patients and Pattern of Illnesses in the Primary Health Centers in A’Seeb Area, Muscat, Sultanate of Oman
Bibliographic record
Abstract
OBJECTIVES: On June 6(th) 2007, a tropical Cyclone Gonu striked the coastline of Oman. The purpose of this study is to compare number of patients and pattern of illnesses between disaster (June 2007) and peace times (June 2006/2008). METHODS: Descriptive comparative analysis of all patients who visited primary health centers in Wilayat A'Seeb during the index days. Electronic database collected from the Health Centers (HC) were grouped into four groups; infection-related, trauma-related, acute non trauma-related, and miscellaneous group. Data were analyzed to find difference of patient influx and disease patterns between disaster and peace times. RESULTS: HC visits during the index days decreased from 9006 in 2006 to 8687 in 2007 then increased to 8786 in 2008. Neither between years variation nor between disaster and peace times difference was found to be statistically significant. The proportion of patient visited the HC due to infection-related illnesses changed from 30% in 2006, 31% in 2007, and 24% in 2008 (p<0.0001). The proportion of patients visited the HC due to trauma-related illnesses had changed from 4% in 2006, to 6.7% in 2007, and to 4.4% in 2008. (p<0.0001). Proportions for acute non trauma-related visits were 27% in 2006, 24% in 2007, and 23% in 2008 (p<0.0001). Miscellaneous group accounted for 38% in 2006, 37% in 2007, and 47% in 2008 (p<0.0001). CONCLUSION: Tropical Cyclone Gonu caused statistically significant increase in percentages of infectious and trauma-related visits. The overall ratios of total visits did not differ from peace times.
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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 | 0.000 | 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".