Benefits and risks of standardization, harmonization and conformity to opinion in clinical laboratories
Bibliographic record
Abstract
Large laboratory systems that include facilities with a range of capabilities and capacity are being created within consolidated healthcare systems. This paradigm shift is being driven by administrators and payers seeking to achieve resource efficiencies and to conform practice to the requirements of computerization as well as the adoption of electronic medical records. Although standardization and harmonization of practice improves patient care outcomes and operational efficiencies, administratively driven practice conformity (conformity to opinion) also has serious drawbacks and may lead to significant system failure. Juxtaposition of the distinct philosophical approaches of physicians and scientists (i.e. "professionalism") versus administrators and managers (i.e. "managerialism") towards bringing about conformity of the laboratory system inherently creates conflict. Despite an administrative edict to "perform all tests using the same methods" regardless of available "best practice" evidence to do so, medical/scientific input on these decisions is critical to ensure quality and safety of patient care. Innovation within the laboratory system, including the adoption of advanced technologies, practices, and personalized medicine initiatives, will be enabled by balancing the relentless drive by non-medical administration to meet "business" requirements, the medical responsibility to provide the best care possible, and customizing practice to meet individual patient care needs.
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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 | 0.062 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".