A STUDY TO ASSESS THE DISTRIBUTION OF SUSPECTED ADVERSE EVENTS FOLLOWING IMMUNISATION REPORTED IN A METROPOLITAN CITY IN INDIA
Bibliographic record
Abstract
Introduction:Adverse Events Following Immunization (AEFI) may be considered as major setback to our immunization efforts and can hinder the optimum utilization of the services provided. Around 14% of parents with a past history of facing a suspected AEFI in any of their children are hesitant to accept future immunizations. Our study aims to understand the distribution pattern of suspected AEFI cases during January 2017 and June 2018. Methods: We conducted a cross-sectional observational record-based study in a Metropolitan city in Maharashtra wherein all AEFI reporting forms namely Case Reporting Form (CRF), Preliminary Case Investigation Form (PCIF), and Final Case Investigation Form (FCIF) containing pertinent data on all AEFI cases that occurred from January 2017 to June 2018 were analyzed using Microsoft Excel 2013 and represented using tables and graphs. Results: The AEFI reporting rate was calculated as 5.8 per 100000 doses administered per year. The total number of AEFIs reported in the year 2017 and 2018 (up to June) were 71 and 58 respectively. 51.16% of the reported AEFIs were febrile seizures, 19.38 % were severe local reactions in the form of abscesses, and 9.3% were afebrile seizures. Twelve deaths were reported during the study period. Injectable Polio Vaccine (IPV) showed the highest rate of antigen-specific AEFI (13.2/100000 doses administered) while measles vaccine showed the lowest rate (2.7/100000 doses administered). Conclusion:Analyzing the distribution of suspected AEFI cases can aid in identifying causal links to known risk factors, inform the development of preventive measures, and enhance immunization coverage.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".