Notice bibliographique
Résumé
Immune thrombocytopenia (ITP) is an autoimmune disorder characterized by a low platelet count and an increased risk of bleeding. The first report of what was possibly ITP dates back to 1557 in a description of a boy "with dark macules, resembling flea bites, had no fever and for several days had bloody discharges, eventually recovering" [1]. Two centuries later, Paul Werlhof described the disorder under the name of Morbus Maculosus Hemorrhagicus, which later became known as M. Maculosus Werlhofii after the physician who first classified it. It wasn't until the ability to readily count platelets and the first successful splenectomy in 1916 that the modern day version of idiopathic thrombocytopenic purpura—recently renamed immune thrombocytopenia—became known. Treatment options for ITP remained fairly limited for the next few decades, consisting mainly of corticosteroids [2], splenectomy, and later intravenous immune globulin [3]; a far cry from the first prescription of "moderate exercise in the open air, a generous diet, and the free use of wine" (Robert Willan, 1808). However, in the last decade, interest in ITP has been renewed mainly because of the introduction of novel treatments including rituximab [4] and the thrombopoietin receptor agonists [5, 6]. This led to increased awareness, clinical trials, and a re-examination of pathophysiology [7]. The result has been a fresh new look at an old disease. And as we begin to peel away the surface of ITP once again, it is becoming more and more evident how little we still know about it—starting with: how common is the problem? To answer this fundamental question, one needs to start with a definition. However, even that is no easy task when it comes to ITP. Perhaps a testament to how far we are from a precise definition is that there still is no diagnostic test, and investigations of patients suspected of having ITP are used to rule out other disorders, rather than to rule in ITP [8]. The lack of disease-defining features is evident; besides thrombocytopenia and the bleeding manifestations that may ensue, there are generally no physical findings, no other blood count derangements, and (under properly controlled conditions) no specific abnormalities on routine bone marrow examinations. On the other hand, some patients can be distinguished by mild splenomegaly, antiplatelet antibodies and increased bone marrow megakaryocytes. Furthermore, serological and molecular studies have demonstrated that some patients have unique cytokine profiles or abnormal regulatory T-cells [9]. These clinical and basic studies suggest that ITP is a heterogeneous disorder with distinct subsets of patients. In this context, the "ITP syndrome" [10] may be the final common pathway for a variety of underlying pathologies characterized by low platelet counts. The nature of the ITP illness poses additional barriers to its proper classification. First, it is relatively uncommon; second, patients are generally not organized into specialized referral clinics rendering enrolment in clinical trials challenging [11]; and third, the disease occurs on a wide spectrum ranging from asymptomatic thrombocytopenia to life-threatening bleeding. Rigorous methods are needed to classify, enumerate, and describe ITP, which invariably will include handling large patient databases. Recent initiatives have provided a framework for the diagnosis and management of patients. Recommendations from the International Working Group on the standardization of terminology provided a new platelet count threshold (below 100 × 109/L platelets) and new definitions of disease stage, severity, and treatment response [12]; and the American Society of Hematology-endorsed evidence-based practice guidelines helped to position new therapies in the context of conventional ones, including splenectomy [8]. In two papers appearing in this issue of the "American Journal of Hematology," Dr. Terrell et al. have gone even further back to basics by devising a strategy to accurately identify patients with ITP from medical records [13], and then applying that strategy to estimate disease frequency in the state of Oklahoma [14]. The authors established levels of evidence for a diagnosis of ITP (definite, probable, or unlikely), reminiscent of clinical grading systems that have been used in other areas of hematology including drug-induced immune thrombocytopenia [15], heparin-induced thrombocytopenia [16], and deep vein thrombosis [17]. They found that limiting the source of data to hematologists' practices and to outpatient visits captured most patients with ITP. Their methods demonstrate rigor in the identification of patients, and highlight the inherent challenges of applying such a strategy to a disease that lacks defining features. For example, by their criteria, the diagnosis of ITP was considered unlikely if a hematologist described "thrombocytopenia" in an adult without specifically stating the diagnosis. Many hematologists might accurately use such a description for patients with mild or moderate ITP. Armed with their validated definition, the authors then set out to determine the frequency of ITP without restrictions, something that had previously only been done using databases of managed health care plans, limited to age groups or by point-prevalence questionnaires [18-21]. The authors found that the average annual prevalence of ITP was 8.1 per 100,000 children and 12.1 per 100,000 adults. The prevalence of ITP increased in older adults and was more common in females, except in the cohort aged 70 years or older, which showed a male predominance. While these numbers apply to the state of Oklahoma, they likely represent an accurate estimate of the burden of ITP in North America, if not most of the developed world. Prevalence and incidence data and the methods used to derive them will now need to be adjusted to disease severity so that the impact of severe ITP can be isolated from patients who may never require treatment. The series of papers by Terrell et al. is a rigorous, back-to-basics approach to an age old disease that is only now being redefined. The landscape of ITP is shifting and refined epidemiological studies such as these are needed to improve classification systems and further our understanding of pathophysiology. Ultimately, such research will help identify those patients who stand to benefit most from treatment.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,006 |
| Communication savante | 0,005 | 0,009 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,017 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,006 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».