Developing GRADE outcome-based recommendations about diagnostic tests: a key role in laboratory medicine policies
Bibliographic record
Abstract
Harmonisation and risk management policies represent key-issues in modern laboratory medicine as they focus on a more patient-centred delivery of laboratory information based on the recognition of the importance of all steps of the total testing process (TTP) for assuring quality and patient safety. However, a further essential step in project aiming to improve the value of laboratory medicine becomes the assessment of the impact of testing on patient-important outcomes. The grading of recommendations assessment, development and evaluation (GRADE) evidence to decision (EtD) frameworks may provide a systematic and transparent approach for translating the best clinical evidence available into healthcare decisions and recommendations. GRADE is a tool appropriate not only for evaluating test accuracy but also for clinical impact, such as mortality, morbidity, symptoms, and quality of life and therefore it should be applied to the outcome research in laboratory medicine. The application of GRADE requires the recognition that a recommendation about the use of test results should result from a balance between the desirable and the undesirable consequences, including non-health related consequences such as resource utilisation, feasibility, acceptability, equity and other factors. GRADE EtDs, represents a fundamental step in projects designed to improve care quality. Patient-physician-laboratory feedback can be assured through the GRADE process, where the team developing the recommendations should include the "three-parties" representatives; clinicians, laboratorians and patient/consumers. This ensures that the laboratory-patient interaction should not be a one-way process only (information from laboratory to patient) but a two-way process, incorporating patient expectations and feedback.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.354 | 0.716 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.017 | 0.013 |
| Research integrity | 0.024 | 0.025 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".