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Enregistrement W1501522998 · doi:10.1111/jtm.12067

Can the Safety of the Yellow Fever Vaccine Be Evaluated by a Retrospective Study of Databases?

2013· letter· en· W1501522998 sur OpenAlexaff
Roger E. Thomas

Notice bibliographique

RevueJournal of Travel Medicine · 2013
Typeletter
Langueen
DomaineMedicine
ThématiqueSARS-CoV-2 and COVID-19 Research
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésMedicineRetrospective cohort studyVaccine safetyYellow fever vaccineDatabaseVirologyYellow feverMedical emergencyImmunologyImmunizationInternal medicine

Résumé

récupéré en direct d'OpenAlex

Nordin et al. assessed the quality of data in two databases, the Vaccine Safety Datalink (VSD) and the US Department of Defense (DoD), for adverse events that occur after yellow fever vaccine.1 The issues are as follows: (1) Can the adverse events be attributed solely to yellow fever vaccine? No, 85% of the adults and 75% of the pediatric subjects in the VSD database received other vaccines and those vaccines were not stated. The authors state that the DoD database did not list other vaccines given (and military personnel are usually given multiple vaccines). Reports of adverse events after vaccination to pharmacovigilance databases usually involve several vaccines given simultaneously, and identifying whether a specific vaccine was responsible is usually impossible. Thus, separating reports for patients who received only yellow fever vaccine from those who received yellow fever and other vaccines is a key solution. (2) Do the authors describe how the events are reported to the databases? No. We are not told if the databases use passive or active reporting. Nor is there any statement about whether other publications or the authors estimated the percentage of vaccine events reported. This is important because underreporting in both active versus passive databases can be substantial.2,3 (3) Is the authors' choice of ICD‐9 codes to classify events appropriate? No. For “allergic and local reactions” they chose 5 ICD‐9 codes, for “visceral” events they chose 25 broad ICD‐9 codes, and for “neurological” events 7 codes for meningitis and encephalitis, 3 for central nervous system demyelinating disease, 1 for hemiplegia and hemiparesis, 4 for seizures and movement disorders, 8 for peripheral and cranial neuropathy, and 4 for altered mental status. Although the ICD‐9 codes could capture some of the adverse events related to yellow fever vaccine, they are nonspecific and do not incorporate the careful algorithms in the CDC or Brighton Collaboration criteria4–8 for adverse events following yellow fever vaccine and would produce substantially different classifications. (4) Do the databases provide enough data for classification of events? No. The authors correctly identified the problems with their data in these four comments: (i) there were “no detailed clinical data needed to determine whether these events met formal YF‐vaccine‐AVD and YF‐vaccine‐AND case definitions”; (ii) “laboratory and radiology data were not available to confirm whether visceral or neurologic events met formal CDC YFSWG or Brighton case definitions”; (iii) “diagnostic codes for symptoms of neurologic and visceral events have unknown sensitivity and specificity for identifying reactions to YF‐vaccine”; and (iv) “further details were not available to determine whether any of these represented true cases of anaphylaxis.” (5) Did the authors independently check data accuracy? No. The VSD and DoD databases are automated and the authors did not perform any checks on data accuracy. (6) For the case–control procedure the authors chose controls on the basis of nonexposure to yellow fever vaccine: “YF‐vaccine‐exposed subjects were compared with randomly selected YF‐vaccine‐unexposed controls.” But did this random selection produce cases and controls with the same number and type of other vaccines? Moreover, the authors stated that “unexposed subjects were not required to have a visit during the period of observation,” and thus no data are available about ICD‐9 events in the unexposed cohort that did not result in a visit to the specific medical organizations. (7) Do the authors' estimates of rates agree with those of other yellow fever databases? The authors cited three yellow fever pharmacovigilance databases (Khromava 2005, Martin 2001, and Martins 2010) for comparison of rates with their data but did not compare their rates with six other databases9–14 or three systematic reviews of yellow fever adverse events.2,3,15 Some data for US military personnel are also reported in the US VAERS database and the data should have been compared with the databases that reported VAERS data. The VSD and DoD databases could potentially be important sources of information about severe adverse events of vaccine, and the authors have performed a helpful service in assessing these databases. Their results illustrate how the databases could be improved by those designing and maintaining them. The author states that he has no conflicts of interest.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,059
score de la tête « metaresearch » (Gemma)0,402
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Méthodes · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,941
Score d'incertitude au seuil0,311

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0590,402
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0030,007
Études des sciences et des technologies0,0010,001
Communication savante0,0030,005
Science ouverte0,0020,001
Intégrité de la recherche0,0050,002
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,064
Tête enseignante GPT0,366
Écart entre enseignants0,301 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeSans objet
DomaineMéthodes
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2013
Routes d'admission1
Résumé présentoui

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