An evaluation of SNOMED CT® in the domain of complex chronic conditions
Bibliographic record
Abstract
OBJECTIVE: To determine the content coverage in SNOMED CT to represent the multidisciplinary terms and concepts in the domain for complex chronic conditions. METHODS: An evaluation of the coverage of multidisciplinary health factors in SNOMED CT for the complex and chronic condition, multiple chemical sensitivity (MCS) is conducted in the study. The methodology included a retrospective audit of patient charts and feedback from multidisciplinary clinicians in the creation of a controlled vocabulary used in the generation of patient profiles for MCS. Clinicians and experts in the field reviewed and tested the vocabulary for its usefulness (scope, specificity and structure) by re-coding three patient profiles using the vocabulary. Cohen's kappa analysis was conducted to determine inter-rater reliability. Cronbach's alpha analysis was conducted to determine the internal reliability of the survey questionnaire. RESULTS: One hundred patient charts and nine clinicians from varying health disciplines participated in the study. SNOMED CT was shown to capture nearly 82% of the concepts spanning multidisciplinary areas of health focus. The nutrition area of health focus had the highest level of exact matches. Furthermore, post-coordination was applied in an attempt to improve coverage of concepts to 75% (of 45 terms) of the missing terms in SNOMED CT. Seventy-five percent (n=9) of the clinicians agreed on the overall usefulness of the vocabulary. CONCLUSIONS: SNOMED CT had a reasonable coverage of the multidisciplinary health concepts required to describe a complex and chronic condition. Standardizing the multidisciplinary vocabulary with reference tag to a widely used reference terminology, such as SNOMED CT to discuss the terms and concepts used may improve the understanding across disciplines and communities of practice. Overall, based on the availability of concepts in SNOMED CT and the feedback from clinicians, the approach looks promising and should be further explored.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".