Bibliographic record
Abstract
Evidence-based laboratory medicine (EBLM) is the use of the current best evidence of the utility of laboratory tests in making decisions about the care of individual patients. This practice means integrating laboratory and clinical experience with the last available external evidence from systematic research. It means that the definition of EBLM focuses on two key elements: experience and evidence from systematic research. Although the term evidence-based medicine (EBM) was created in Canada at Mc Master University by a group lad by Dr Gard Guyatt, there are various claims as to the origin of its practice. Regardless of its origins, many factors have come together over the past 30 years to drive the movement to EBM. One factor is those individual physicians, faced with numerous medical informations; the second factor is the global phenomenon of increasing health care costs and third is that patients who have generally more education, want the best in diagnostics and therapies. It means that evidence-based medicine has been driven by the need to cape with information overload, by costcontrol, and by public impatient for the best in diagnostics and treatment. Clinical guidelines care maps, and outcome measures are quality improvement tools for the appropriateness, efficiency and effectiveness of health services. Laboratory professionals must direct more effort to demonstrating the impact of laboratory tests on a greater variety of clinical outcomes. Evidence-based laboratory medicine aims to advise clinical diagnosis and management of disease through systematic researching and disseminating generalisible new knowledge that meets the standard of critical review on clinically effective practice of laboratory investigations. In laboratory medicine, the use of tests increases; new tests are constantly introduced, but "old" tests are seldom removed from the repertoire. This, together with limited public funds for the health care should underline the challenge for laboratory professionals to provide evidence for the utility of different tests. This practice means integrating laboratory and clinical experience with the best available external evidence from systematic research therefore, it is important that advice given by laboratory medicine professionals are sound and based on evidence in the pre-analytical, analytical, and post-analytical phases of the diagnostic process. This paper provides an insight into the rationale, methodology and the phases of the EBLM.
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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 | 0.394 | 0.588 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.028 | 0.021 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.041 | 0.045 |
| Open science | 0.012 | 0.029 |
| Research integrity | 0.034 | 0.042 |
| Insufficient payload (model declined to judge) | 0.011 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".