Evidence mapPaperPMID 39685749Full record

ArticleJournal of clinical medicine2024

The WHO Algorithm for Causality Assessment of Adverse Effects Following Immunization with Genetic-Based Anti-COVID-19 Vaccines: Pitfalls and Suggestions for Improvement.

Paolo Bellavite, Alberto Donzelli, Ciro Isidoro

Abstract read
In one paragraph

Article in Journal of clinical medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Paolo BellaviteIndependent Researcher, 37134 Verona, Italy.ORCID 0000-0002-5086-3504
Alberto DonzelliFoundation Allineare Sanità e Salute, via Ricordi 4, 20131 Milano, Italy.ORCID 0000-0002-4365-6814
Ciro IsidoroDepartment of Health Sciences, Università del Piemonte Orientale, Via P. Solaroli 17, 28100 Novara, Italy.ORCID 0000-0002-5494-3034

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clarifying and differentiating the causes of diseases is an essential step in any clinical activity, but it takes on particular relevance and complexity in the case that arise following vaccinations. The WHO has proposed a protocol that uses a list of specific questions about vaccine-related adverse events and an algorithm for making a judgement. Here, we analyze and discuss the important limitations of this protocol when applied to the new genetic-based anti-COVID-19 vaccines, particularly once dealing with rare and unexpected pathological events. The main controversial aspects concern: (a) the prevailing consideration of other possible causes; (b) the biological plausibility and the choice of an appropriate time window to consider adverse effects possibly caused by vaccines; (c) the reference to scientific literature, which may be very limited and often controversial in early stages of introducing new vaccines because of the short period of observation; (d) the final classification of the algorithm into only three classes, which leaves ample space for the "indeterminate" category. Failure to address these issues may lead to distorted pharmacovigilance reports with significant consequences on the benefit/harm assessment. In anticipation of possible future pandemics managed with new vaccines, the WHO algorithm needs to be revised with appropriate protocols for monitoring and evaluation of adverse effects that take into account the novel mechanism of action and real-world epidemiological data.

Indexed as

adverse effects following immunizationcausality assessmentCOVID-19 vaccinedifferential diagnosispharmacovigilancevaccine safetyWHO algorithm

Identifiers

PMID39685749
PMCPMC11642405

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.