{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:16:37Z","timestamp":1786115797846,"version":"build-2736575974"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background and aims<\/jats:title>\n                    <jats:p>Ensuring the documentation quality of spontaneous reports in the Vaccine Adverse Event Reporting System (VAERS) is important for signal detection, yet targeted tools for evaluating vaccine-specific reporting quality remain lacking. This study proposes the Vaccine Reporting Quality Score (VRQS)\u2014a transparent, exploratory evaluation system\u2014and applies it to a large-scale VAERS dataset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      The preliminary VRQS aggregates 13 items into two primary domains: Completeness (items 1-10) and Clinical Informativeness (items 11-13), using equal weighting as an initial exploratory index. Using the VAERS 2025 dataset, we established a strict 1-to-1 patient case unit of analysis (N\u00a0=\u00a040,285 unique case reports) to evaluate demographic, administrative, and clinical density. Multivariable linear and ordinal logistic regressions were executed using independent case characteristics, with all overlapping score components removed\n                      <jats:italic>a priori<\/jats:italic>\n                      to eliminate structural circularity.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The mean overall quality score was 9.04 \u00b1 1.96, with 57.54% of unique reports falling into a medium-quality tier (scores 8\u201310). Product identifiers, such as vaccine type (97.92%), were highly prevalent, whereas informative laboratory data were present in only 16.18% of records. Distinct, descriptive documentation profiles emerged: spontaneous records displayed high completeness but low informativeness, whereas professional entries and serious adverse events (SAEs) featured dense clinical metrics alongside missing administrative checkfields. In re-specified, non-circular multivariable models, complex clinical factors such as pre-existing allergies (b=0.832, p&amp;lt;0.001) and concurrent medications (b=0.463, p&amp;lt;0.001) were independently associated with higher quality score.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed VRQS framework demonstrates that administrative documentation density does not inherently correlate with clinical informativeness. Rather than acting as a validated tool for causality assessment, the VRQS provides a transparent, preliminary approach for report prioritization and screening pending formal external validation against global benchmarks.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1871141","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T05:33:51Z","timestamp":1785994431000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and application of the vaccine reporting quality score to evaluate the vaccine adverse event reporting system"],"prefix":"10.3389","volume":"17","author":[{"given":"Zongkai","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Physical Education, Hunan University","place":["Changsha, Hunan, 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