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Enregistrement W2917187402 · doi:10.1373/jalm.2017.025684

The Academic Clinical Laboratorian: Fact or Fiction?

2019· article· en· W2917187402 sur OpenAlexaff
Gena Ibrahim, George M. Yousef

Notice bibliographique

RevueThe Journal of Applied Laboratory Medicine · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueHealth and Medical Research Impacts
Établissements canadiensUniversity of TorontoSt. Michael's Hospital
Organismes subventionnairesnon disponible
Mots-clésChemistEngineering ethicsFocus (optics)Philosophy of scienceSociologyEpistemologyPhilosophyEngineeringChemistry

Résumé

récupéré en direct d'OpenAlex

There is an ongoing question of whether laboratory professionals (pathologists, chemist, microbiologists, geneticists, and molecular biologists) should be involved in research activities. Some argue that they should focus on their clinical duties and let research be handled by basic scientists who are more professionally equipped for this job. When I think of this paradigm I am reminded of James K. Feibleman, Philosophy Chair at Tulane University, who writes “By 'applied science' is meant the use of pure science for some practical human purpose. Applied science, then, is simply pure science applied. But scientific method has more than one end; it leads to explanation and application” (1). In my opinion, laboratorians should be active members of a research team. Not only that, research activity should be a core mandate of their job profile. In this new epoch of precision medicine, the role of laboratory professionals is rapidly expanding as they become critically involved in patient management. With recent advances in molecular analysis, up to 50% of management decisions will be based on laboratory test results (2). In addition to diagnostic testing, the laboratory applies valuable prognostic and predictive information. Molecular testing can also provide disease risk assessment, monitor disease progression, and give useful information to adjust medication doses, putting laboratorians at the center of the patient management team (3). Laboratory scientists capture a number of unique aspects that are critical for research success and quality. As the owners of valuable tissue and biofluid material, laboratory personnel are the best to accurately annotate specimens. Let us take pathology as an example. Getting a sample of kidney cancer tissue for research requires accurate assessment of the histologic subtype (according to the most recent classification system), choosing a representative area with tumor tissue without necrosis or hemorrhage, and assessment of stromal and normal tissue contamination. Added to this, being responsible for performing the test, they have a better insight about technical specifications, including the variation between different platforms, specimen collection and storage protocols, and a number of preanalytical, analytical, and postanalytical considerations. This is even more important for molecular testing when the cutoffs between positive and negative results can be less clearly defined. A clinical laboratory might not be fully equipped for scientific discovery, but it is ideally suited for scientific application. However, there are more aspects that the word “research” encompasses, including a number of fields where the clinical laboratory can get engaged in and provide a meaningful contribution. Examples can include translational research; looking for disease biomarkers and biological stratification of patients with pathological conditions can be an attractive field that is well-suited for laboratorians. Another interesting field that pathologists can pioneer is morphological research. They can provide a new dimension on subcellular localization of biomarkers. Morphological parameters can also provide very useful information for disease classification, grading, and assessment of hereditary disease risk (4). Famous successful examples are development and application of the Gleason grading of prostate cancer and the Fuhrman grading of kidney tumors based on morphology. In addition, laboratory clinicians can contribute to clinical trials by leading companion biomarker testing studies. Predictive biomarker discovery is becoming an important component of clinical trials. Research, in the broader sense, includes educational applications, which is attracting more attention in this area of digital and simulated education. Quality assurance research represents another new niche for laboratory clinicians. Another attractive new research direction is “health utilization” research that involves appropriate test ordering and analysis of the cost-effectiveness of laboratory testing. We have to realize, however, that engaging in a research activity is easier said than done. A universal problem for researchers is the availability of funding. For this, laboratorians should think of innovative approaches, including seeking targeted agencies that are interested in a specific field of research. Also, teaming up with basic scientists, public health investigators, bioinformations, and clinicians will enhance grant success. In recent years, it became also evident that partnership with industry can be a mutually beneficial successful strategy. Looking for philanthropic funding has been successful in supporting research (5). Another essential issue that is worth highlighting is the need of mentorship. Research is not an amateur job anymore, and, for it to be done efficiently, there is a need for structured mentorship and training on how to write a successful grant and how to get your paper published. Finding protected time for a research is another chronic obstacle that faces clinicians, but it didn't hinder the ability of many to pursue successful research careers. Research is a journey with clear challenges, but once you decided to accept the challenge, you will learn from your successes and mistakes and you will realize that building a researcher is an ongoing process that involves multiple steps. A tough but very interesting and thoughtful path to consider.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,023
score de la tête « metaresearch » (Gemma)0,087
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,023
Score d'incertitude au seuil0,123

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0230,087
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,003
Études des sciences et des technologies0,0060,057
Communication savante0,0100,027
Science ouverte0,0040,005
Intégrité de la recherche0,0180,031
Charge utile insuffisante (le modèle a refusé de juger)0,0050,003

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.

Tête enseignante Opus0,122
Tête enseignante GPT0,473
Écart entre enseignants0,350 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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