In-house Calibration of the International Sensitivity Index or Calibration Curve for Determination of the International Normalized Ratio
Bibliographic record
Abstract
CONTEXT: The international normalized ratio (INR) has been used since 1983 to standardize prothrombin time results for patients on oral anticoagulants. However, significant interlaboratory variations have been noted. Attempts have been made to address these differences with the use of instrument-specific International Sensitivity Index (ISI) values and in-house calibration of ISI values. OBJECTIVE: To assess the performance of laboratories using a calibration curve for INR testing. DESIGN: Attempts to improve performance of the INR include the use of instrument-specific ISI values, model-specific ISI values, in-house calibration of ISI values, and more recently, the preparation of a calibration curve. Several studies have shown an improvement in performance using these procedures. In this study of licensed laboratories performing routine coagulation testing in the Province of Ontario, Canada, the determination of the INR by a calibration curve was compared with the laboratories' usual method of assessment. These methods were subsequently analyzed by comparing the results to instrument-specific ISI, model-specific ISI, and in-house calibrators. International normalized ratios derived by both methods were analyzed for accuracy and precision. The stability of a calibration curve was also investigated. RESULTS: Performance of INR testing has improved with use of a calibration curve or in-house calibrators. CONCLUSION: The results confirm that either in-house calibrators or the calibration curve improve performance of INR testing. The calibration curve may be easier to use and appears stable up to 4 months.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".