Epidemiological Characteristics of Deceased H1n1 Cases in Civil Hospital, Gandhinagar During First Quarter Of 2015
Bibliographic record
Abstract
Introduction: In preparation for future waves of H1N1 influenza, determining the correlates of the severity of disease may be very important. Methodology: A retrospective, descriptive study was carried out at the Civil Hospital, Gandhinagar which included all the deceased patients of Influenza A H1N1 from 1st January 2015 till 31st March 2015. The admission history and their medical records including certified cause of death of all deceased H1N1 patients were collected and assessed for clinico-epidemiological details. Result: Mean age of fatal cases was 51.4 years, male to female ratio was 1:0.9 and 68% resided in Gandhinagar rural area. Majority (68%) were referred from private hospital. Almost 50% of the deceased had some form of comorbid conditions. Fever (78.9%), breathlessness (73%) and dry cough (73.7%) were reported mainly among the deaths due to H1N1. Median time, from onset of symptoms to date of admission was 4 days; whereas that from admission to death was 2 days. Conclusion: Adult age, residing in rural area, delayed referral from private practitioner, presence of comorbid conditions were found to the few reasons associated with deaths due to H1N1.
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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) | 0.002 | 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".