Who Profits From Uncritical Acceptance of Biased Estimates of Vaccine Efficacy and Safety?
Bibliographic record
Abstract
We read with great interest the analysis by Mello et al.1 on how Merck & Co., Inc. (Merck) influenced state human papillomavirus (HPV) vaccination policymaking. The exclusive reliance on Merck for scientific information on behalf of the legislators is unfortunate, especially in the light of independent research which has repeatedly warned that drug companies may manipulate clinical trial designs and subsequent data analysis and reporting to make their drugs look better and safer.2–4 Indeed, careful scrutiny of Gardasil clinical trials shows that their design, as well as data reporting and interpretation, were largely inadequate.4–6 Given this, the widespread public optimism regarding Gardasil’s clinical benefits appears to rest on an extremely weak base built on a number of untested assumptions and significant misinterpretation of factual evidence. For example, the claim that Gardasil vaccination will result in approximately 70% reduction of cervical cancers7,8 is made despite the fact that the clinical trial data have not demonstrated to date that the vaccine has actually prevented a single case of cervical cancer (let alone cervical cancer death),4 nor that the current overly optimistic surrogate marker–based extrapolations are justified.6 A second equally fallacious claim is that lifelong protection arises from three vaccine doses,7,8 although clinical trial follow-up data do not extend beyond five years.9 The third claim is that Gardasil may induce only minor side effects of negligible clinical importance,7,8 although such conclusions are only supported by highly flawed safety trials design.4,10 Additionally, we note evidence of biased and selective reporting of results from clinical trials, that is, exclusion of particular vaccine efficacy figures from peer-reviewed publications, such as those related to study subgroups in which efficacy might be lower or even negative.4,5 All of the above issues suggest that the information presented by Merck to the public and the various state legislators concerning Gardasil safety and true prophylactic value were incomplete and inaccurate and thus inevitably misleading, particularly in light of data from various vaccine safety surveillance systems and case reports that continue to raise significant concerns regarding the safety of Gardasil (Table 1).4 TABLE 1— Age-Adjusted Rate of Adverse Reactions (ADRs) Related to Gardasil Compared With All Other Vaccines in the United States Reported to the Vaccine Adverse Event Reporting System (VAERS) as of March 25, 2012. Keeping in mind that “the primary interest of a pharmaceutical company is developing and selling pharmaceutical product,”1 one must ask whether rational vaccine policy decisions should be based on conclusions derived from an uncritical acceptance of flawed vaccine safety and efficacy estimates provided by the vaccine manufacturer. Failure to adhere to principles of evidence-based medicine with respect to Gardasil promotion and vaccination policymaking inevitably raises the question of whether we have learned anything from the Vioxx debacle.
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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 | 0.175 | 0.584 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.013 | 0.031 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.034 | 0.045 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".