Application of the TML method to big data analytics and reference interval harmonization
Bibliographic record
Abstract
Abstract Significant variation in reported reference intervals across healthcare centers and networks for many well-standardized laboratory tests continues to exist, negatively impacting patient outcomes by increasing the risk of inappropriate and inconsistent test result interpretation. Reference interval harmonization has been limited by challenges associated with direct reference interval establishment as well as hesitancies to apply currently available indirect methodologies. The Truncated Maximum Likelihood (TML) method for indirect reference interval establishment developed by the German Society of Clinical Chemistry and Laboratory Medicine (DGKL) presents unique clinical and statistical advantages compared to traditional indirect methods (Hoffmann and Bhattacharya), increasing the feasibility of developing indirect reference intervals that are comparable to those determined using a direct a priori approach based on healthy reference populations. Here, we review the application of indirect methods, particularly the TML method, to reference interval harmonization and discuss their associated advantages and disadvantages. We also describe the CSCC Reference Interval Harmonization Working Group’s experience with the application of the TML method in harmonization of adult reference intervals in Canada.
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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 | 0.078 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".