Separating Myths from Reality for Vaccines Causing Adverse Events Following Immunization: An Interview with Manish Sadarangani
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dr Manish Sadarangani is Director of the Vaccine Evaluation Center at the BC Children's Hospital Research Institute and an Assistant Professor in the Division of Infectious Diseases, UBC Department of Pediatrics. He completed his undergraduate medical and pediatric training in Cambridge, Oxford and London in the UK. He then completed his DPhil with the Oxford Vaccine Group in the UK, developing novel vaccine candidates for protection against capsular group B meningococcal disease, and completed a fellowship in pediatric infectious diseases in Vancouver in 2013 before returning to Oxford to work as a pediatric infectious diseases physician. His research links clinical trials with basic microbiology, immunology and epidemiology to address clinically relevant problems related to immunization and vaccine-preventable diseases.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it