Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.057 |
| Scholarly communication | 0.010 | 0.027 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.018 | 0.031 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".