Editorial: What's in a Name, or the Impact of Misnomers in Endocrine Research
Bibliographic record
Abstract
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-
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".