Bibliographic record
Abstract
The current health care environment of cost-cutting highlights the need to reinforce the contribution of laboratory medicine to improvement in health care. This must be a patient-focused activity using continuous quality improvement, a familiar concept in laboratory practice. Involvement in the creation of clinical practice guidelines, care maps, and outcome measures will place laboratory medicine in the circle of continuous quality improvement. The laboratory must provide strong evidence that tests contribute to better overall resource utilization. Laboratory Information Systems can be used to better integrate laboratory data with clinical, diagnostic, pharmaceutic, statistical, and financial information. Improving laboratory utilization requires clear demonstrations of appropriate versus inappropriate laboratory use, and instructions on implementing appropriate use. The education of laboratory professionals should include search strategies, understanding the diagnostic accuracy of medical tests, and the application of systematic reviews and meta-analysis. With the rapid increase in the data base supporting evidence-based laboratory medicine, there is a significant challenge in translating the existing knowledge into practice. There is also a need for a cooperative strategy between the diagnostics industry and the laboratory medicine profession to provide evidence of the added value of laboratory testing. There is a significant role in developing the academic basis of the unique aspects of evidence-based laboratory medicine.
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 | 0.086 | 0.160 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".