Experiences mapping a legacy interface terminology to SNOMED CT
Bibliographic record
Abstract
BACKGROUND: SNOMED CT is being increasingly adopted as the standard clinical terminology for health care applications. Existing clinical applications that use legacy interface terminology need to migrate to the preferred SNOMED CT standard. In this paper, we describe our experience and methodology for mapping concepts from a legacy system to SNOMED CT. METHODS: Our approach includes the establishment of mapping rules between terminologists and back and forth collaboration of the mapped results through one or more iterations in order to reach consensus on the final maps. RESULTS: We highlight our results not only in terms of the number of matches, quality of maps, use of post-coordination, and multiple maps but also include our observations about SNOMED CT including inconsistencies, redundancies and omissions related to our legacy mapping. CONCLUSION: Our methodology and lessons learned from this mapping exercise may be helpful to other terminologists who may be similarly challenged to migrate their legacy terminology to SNOMED CT. This mapping process and resulting discoveries about SNOMED CT may further contribute to refinement of this dynamic, clinical terminology standard.
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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 | 0.037 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".