University-age vaccine mandates: reply to Lam and Nichols
Bibliographic record
Abstract
For the first comparison, we weighed predicted hospitalisations prevented by one booster dose of BNT162b2 with vaccine-associated SAEs from the manufacturer's randomised trial (3/5055).3 We found that the rate of expected SAEs would outweigh the benefits of the booster against hospitalisation by at least 18-fold. Lam and Nichols suggest that this was an inappropriate comparison, as not all SAEs result in hospitalisation. However, the definition of SAE as used in the trial included death, hospitalisation, disability, permanent damage, life-threatening event or condition, which required medical or surgical intervention to prevent a serious outcome.4 While all comparisons include some degree of incommensurability, comparing these SAEs with hospitalisations prevented by the booster is more reasonable than Lam and Nichols' suggestion of comparing SAEs to infections prevented. The COVID-19 infection hospitalisation risk in this age group was <0.5% (or <1/200)5 even prior to widespread immunity, thus comparing SAEs to infection risk is entirely inappropriate. Furthermore, a booster dose will only offer transient (if any) protection against infection6 and cumulative infection rates in the boosted versus unboosted are expected to be approximately the same after several months.6 We disagree that an SAE from vaccination should be considered equivalent to or in any way comparable with postponing an infection for a few months.
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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 | 0.023 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.040 | 0.058 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".