Bibliographic record
Abstract
OBJECTIVES: While reference limits are foundational to interpreting clinical laboratory tests, they may not correspond to the actual values triggering clinical response. We propose to measure this using clinical action curves, which plot test values against an indicator of clinical action. METHODS: We selected repeat test ordering as a quantifiable, objective, useful measure that is readily calculable using available laboratory data. Using all results in Calgary in 2010-2011 for eight analytes, clinical action curves for each analyte were plotted as the relationship between index test value and retesting hazard, modeled using Cox proportional hazards with restricted cubic splines. Clinical action limits were defined where retesting hazard rose 38% above baseline (25%-50% considered). RESULTS: In general, clinical action increased before the reference limits, and clinical action limits were narrower than reference limits. However, some reference limits showed no increased clinical action and may thus be ignored in practice. CONCLUSIONS: Clinical action curves and limits provide practical, objective tools for describing physician responses to test values. Results suggest that many normal results are treated as abnormal and vice versa; such discrepancies require further scrutiny and ultimately reconciliation via altered reference ranges or altered practice patterns.
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 | 0.025 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".