Economic evidence in decision-making process in laboratory medicine
Bibliographic record
Abstract
Laboratory data play a pivotal role in the clinical decision-making process. Major transformations have occurred in laboratory medicine in recent decades. To face the economic pressures, hospital laboratories are forced to enhance efficiency. Decisions on policy and practice take place at many levels. However, decision-making often does not follow Evidence Based Laboratory Medicine principles. Also, the literature shows limited influence of economic evaluations on health care decisions and diagnostic processes. Several barriers to the use of economic evaluation in decision-making process have been identified, and guidelines tend to focus on issues of effectiveness and have not explicitly considered broader issues, particularly cost. As an example, we analyzed recommendations on the use of brain natriuretic peptide (BNP) or N-terminal fragment of the prohormone BNP (NT-proBNP) in patients with chronic heart failure. All guidelines recommend the use of BNP if available. Nevertheless, none included economic data explicitly, even if economic information exists in the literature. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group, propose using a balance sheet approach, one way of helping decision makers to explicitly consider resource use along with other outcomes when making recommendations. Key aspects of GRADE, such as the explicit presentation of information and the quality evaluation of the economic data can help overcome barriers in the use of economic evaluations in the decision-making in process. This approach can help to give health decision makers, clinical guideline panels and patients, a better appreciation of the overall health benefits, harms and costs of laboratory tests.
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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 | 0.343 | 0.667 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".