Bibliographic record
Abstract
The role of diagnostic laboratory testing in healthcare is evolving. The challenge facing the new era of medicine is the appropriate implementation of new tools and technologies to improve patient care in a cost-effective and sustainable manner. Unprecedented expectations to detect disease earlier and effectively treat all aspects of health and wellbeing create demands for testing which may be premature. Test ordering patterns among physicians are subject to psychological factors such as a desire for certainty and risk aversion, as well as fears of patient dissatisfaction, and litigation. The path towards optimizing the utilization of diagnostic laboratory tests must include strategies to minimize non-contributory testing. Appropriate application and sound clinical reasoning are essential to mitigating overdiagnosis, exponential costs on the healthcare system and unnecessary suffering on the part of the patient. Reducing non-contributory laboratory testing practices would allow for the reallocation of resources toward the protection of imperative practices and the advancement of strategies in preventative and individualized medicine. With thoughtful deliberation and constructive conversations among all stakeholders these challenges, pressures and disruptions have the potential to create innovative strategies to optimize the use of diagnostic laboratory testing in healthcare.
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 | 0.036 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 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".