Editorial: The art of reducing futile biomedical research
Notice bibliographique
Résumé
Biomedical research in its several forms has led to new ways to prevent, diagnose and treat illness. As a result, currently people around the world are living longer. For example, in 1900, the average life expectancy of a newborn was 32 years by 2021 it jumped to 71 years (1). To achieve this, billions of dollars, millions of people, and an enormous amount of health resources are invested in biomedical research globally each year. Although these endeavors have achieved substantial improvements at individual and population health and well-being, much more could be done if futility in research is avoided/corrected. Historically, futility is often used to indicate that a clinical trial is unlikely to achieve its original goal ( 2). Yet, futility can be found in any biomedical research-related activity, and it is translated as waste. For example, it is estimated that 85% of global research investment every year is wasted (3). In 2012, the United States, Canada, Europe, and the Asia-Pacific region invested a total of US$268.4 billion in biomedical research and development (4). This research waste can be found in three classes: i) research that is flawed, poorly conducted, or poorly reported, ii) research that is incapable of reaching a conclusion different than from what is known, and iii) research that contributes to the imprecision of effect estimates (by adding methodological heterogeneity, underpowered findings, or technological diversion) (5). The present Research Topic, The Art of Reducing Futile Biomedical Research, aimed to draw the attention of key stakeholders and provide useful evidence to address this important public health problem, with a particular focus on the first class of waste (e.g., choosing the wrong questions or low priority for research). Therefore, we present four high-quality research articles, including three original contributions and one perspective article.The original research articles in this Research Topic offer valuable insights into how to minimize futile biomedical research efforts using empirical data from Ecuador, England, and France. The first study, "One hundred years of Ecuadorian biomedical scientific output and its association with the main causes of mortality: a bibliometric study," mapped the scientific production in biomedical research in Ecuador and analyzed its association with the main local disease burden. Among their findings highlights the predominance of observational studies (79%), that private universities were the main producers of biomedical research in the country compared to public institutions, and that 12.5% of the total scientific output (excluding COVID-19-related publications) was dedicated to addressing the main causes of mortality (Sisa et al.). This lack of prioritization of research areas aligned with national needs contributes to the waste of biomedical research in low-and middle-income countries and has been reported elsewhere (6). The second original study, "The therapeutic futility paradox: insights from oncological drug litigation in Ecuador," analyzed the access to oncological drugs through judicial processes (21 were individual claims and 5 were collective actions) against the Ecuadorian Ministry of Public Health. Of interest is the striking discrepancy between clinical evidence and judicial decisions. Thus, in more than 90% of the judicial processes, the arguments used were improvements in quality of life, overall survival and disease progression; however, according to the United States Food and Drug Administration and the European Medicines Agency, only 18.7% of the requested drugs demonstrated such benefits in pivotal clinical trials (Mena Ayala et al.). This significant gap found between scientific evidence and judicial decisionmaking showed that even waste can be found when relevant quality research is not effectively applied in a clinical/appropriate context (7). The third original article published in this Research Topic by Guillemot and colleagues, entitled "Understanding trends in osteoporosis drug prescribing: implications for reducing futile biomedical research," evaluated trends in osteoporosis drug prescribing in France and England and how medicalization, pharmaceuticalization, and standardization influence the pharmaceutical management of osteoporosis. The findings show a decline in osteoporosis drug prescribing that may be influenced by changing perceptions of aging, policy influences, and healthcare provider decision-making in France and England. This study adds to the growing body of evidence to reduce futile biomedical research, as understanding prescribing trends would allow efficient resource allocation and improve patients' health and quality of life.A perspective article in this Research Topic challenged the "publish or perish" paradigm, entitled "Integrity at stake: confronting "publish or perish" in the developing world and emerging economies," the pressure to publish within the academic community has catalyzed unethical practices, including the sale of authorships, the proliferation of paper mills, and the use of artificial intelligence to produce scientific publications, which ultimately jeopardize the credibility and public trust in the scientific community. Therefore, this article provides an overview of how these practices contribute to the waste of biomedical research endeavors. For instance, fraudulent research consumes valuable time of editors, peer reviewers, and journal staff that could otherwise be invested in analyzing genuine/meaningful submissions (Vasconez-Gonzalez et al.).Futile biomedical research occurs indistinctly of setting location or income and in a variety of forms. The present Research Topic, thanks to the contribution of all the authors, reviewers, and topic editors, shows how the answering of research questions without alignment with national needs, using inappropriate clinical evidence or unethical research practices contribute to the waste in biomedical research. Although this problem is not new, and it has been on the public awareness for a while, its detrimental effects to the advance of science and the well-being of people is enormous (7,8). Paraphrasing the words of John C. Bailar, we can argue that there may be greater danger to the public welfare from scientific dishonesty than from almost any other form of dishonesty (9). We hope that this Research Topic and its valuable findings will inspire further research to tackle this evolving and timely public health issue.
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 | 0,013 | 0,073 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,002 |
| Méta-épidémiologie (sens large) | 0,005 | 0,004 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,012 | 0,009 |
| Science ouverte | 0,006 | 0,002 |
| Intégrité de la recherche | 0,023 | 0,030 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,032 | 0,024 |
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 ».