Body Counts: Medical Quantification in Historical and Sociological Perspectives (review)
Notice bibliographique
Résumé
Reviewed by: Body Counts: Medical Quantification in Historical and Sociological Perspectives Keith R. Benson Gerard Jorland, Annick Opinel, and George Weisz, editors. Body Counts: Medical Quantification in Historical and Sociological Perspectives. McGill-Queen's University Press2005. x, 417. $34.95 Since the advent of the biomedical model for medicine, early in the twentieth century, a commonplace question has been posed: Is medicine [End Page 155] an art or a science? Given the positivistic turn of science that embraced mathematics at the same time, the question actually centres on the increasing role of quantification in medicine as it adopted science, the theme of Body Counts. Surprisingly, to date there have been few comprehensive historical examinations of this important topic, so this volume, based on the proceedings from a 2002 meeting at the Musée Claude-Bernard, will fill an important niche. There are seventeen individual submissions to Body Counts, covering a wide range of examples of the impact of quantification on modern medicine dating from the eighteenth century. Several document early examples of 'medical arithmetic' or statistics in medicine, ranging from Ulrich Tröhler's examination of mathematics used by English physicians in the eighteenth century (before the more well-known Paris school of quantification), Harry Marks's discussion of Parisian arguments over inoculation in the eighteenth century, Andrea Rusnock's fascinating comparative compilation of infant mortality rates in England and France at the end of the eighteenth century, Ann La Berge's re-examination of the 1837 debates in Paris concerning attempts to make disease more objective through the use of mathematics, and Volker Hess's discussion of how the use of body temperature contributed to quantification in nineteenth-century medical practice. In the main, these contributions represent careful and insightful examinations of unusual or unique examples of quantification prior to the twentieth century. However, they do not point to episodes that necessarily led to the later full inclusion of mathematics in medicine. The same almost anecdotal, but nonetheless important, nature characterizes the contributions of Christiane Sinding's examination of the establishment of precise levels of insulin use for diabetics, Ilana Löwy's treatment of the forgotten and pioneering pedologist Jozefa Joteyko (who championed numerical arguments in physiological medicine), Gérard Jorland's evaluation of how Parisian physicians used mathematics to dispute Ignaz Semmelweis's observations on puerperal fever, and Michael Donnelly's re-appreciation of William Farr's nineteenth-century work in public health. Many of these chapters are laudable, but the influential value of the book centres on contributions that focus more narrowly on the twentieth century. These examine the role mathematics played in the evolution of notions of disease from the perspective of simple causal agents (bacteria, for example) to notions of disease being the result of complicated and interconnected factors, including social and behavioural explanations. This latter understanding was accompanied by the formation of medical epidemiology, arguably the most dramatic and characteristic change in modern medicine and public health. Peter Keating and Alberto Cambrosio document the impact of this change on [End Page 156] pathology, as it inextricably moved from qualitative assessments to more quantifiable measures. Mark Parascandola, Iain Chalmers, Luc Berlivet, Lion Murard, Nicolas Dodier, and George Weisz all provide even more concrete examples. As several of these contributions point out, quantifiable measures that characterize twentieth-century epidemiology appear to be less dependent upon the growing importance of quantification in nineteenth-century science, most typically in the form of statistics. What currently is known as randomized clinical trials bears little direct debt to the work of Pearson or Fisher, as Chalmers points out. Here, the work of Bradford Hill is mentioned, as is a reference to Richard Doll (he was knighted for his contributions to medical epidemiology). Both Hill and Doll beg for much more historical attention, since their mathematical insights in the 1950s helped to usher in novel ways to understand health. It was this type of new thinking that led to the understanding of the relationship between cigarette smoking and lung cancer (Parascandola), attempts to create international indices for health (Murard), and AIDS (Dodier). In fact, as George Weisz relates at the conclusion of the book, it is this approach that has...
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».