Editorial: What's in a Name, or the Impact of Misnomers in Endocrine Research
Notice bibliographique
Résumé
The simplest definition of the word “misnomer” is that of a wrong name or inappropriate designation for a person, a place, or an object. Alternatively, in the case that interests us in this editorial, it can be a gene or its gene product. In everyday life, the use of misnomers, such as referring to a small meteorite entering the earth atmosphere as a “shooting star,” often has no practical consequence. In fact, misnomers such as this example can add a dose of poetry in our lives, allowing us to dream of distant universes for a brief moment. However, in medicine, the use of misnomers to identify a symptom or a disease can have more serious consequences, such as misdiagnosis or incorrect treatments, even by welltrained physicians. A quick search of PubMed yields a long list of such occurrences that includes, for example, the misnomer “lupus anticoagulant,” a coagulation inhibitor originally identified in patients with systemic lupus erythematosus, but its presence is actually associated with thromboembolic events that may strike in otherwise healthy individuals (1). In medical research, the consequences of using misnomers might be less dire but, nonetheless, can lead some misguided investigators to pursue research projects in the wrong direction, misinterpret the results of their investigations, alter the conclusion of their work, and at worst, perpetuate false concepts and deceptive hypotheses. Although the routine use of misnomers is more often an annoyance than a critical threat to medical research, this phenomenon can stunt progress and further demonstrates a certain lack of rigor in the scientific process. In the biological sciences, misnomers can originate from the fact that a gene/protein has two or more possible functions, but that the name in use reflects a minor or even a physiologically irrelevant function of the gene/protein rather than its true role(s). Misnomers also arise because the entity named received its designation based on the first alleged function assigned to it, long before its true function was recognized. A paradigm for these occurrences is the superfamily of nuclear receptors, in which misnomers are widespread. The problems for this group of genes are further compounded in that many nuclear receptors were codiscovered by different groups, each using a different name for the same receptor. In 1999, researchers in the field agreed to a new nomenclature to unambiguously identify each nuclear receptor (2). The nomenclature was based on subfamilies and groups of receptors as part of a phylogenetic tree that connects all known nuclear receptor sequences, and each receptor was assigned an alphanumeric name analogous to a “postal code.” It was recommended that the receptor(s) be identified by the official name(s) at least once in a manuscript, preferably in the abstract and/or the introduction. Once the receptor was matched to its “official name or code,” authors were then encouraged to use the trivial name for the remainder of the manuscript. More on that subject later. Although the use of this nomenclature restored some order in this dysfunctional family, not all authors com-
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».