Clinical vocabulary as a boundary object in multidisciplinary care management of multiple chemical sensitivity, a complex and chronic condition
Bibliographic record
Abstract
BACKGROUND: Research has shown that accurate and timely communication between multidisciplinary clinicians involved in the care of complex and chronic health conditions is often challenging. The domain knowledge for these conditions is heterogeneous, with poorly categorized, unstructured, and inconsistent clinical vocabulary. The potential of boundary object as a technique to bridge communication gaps is explored in this study. METHODS: A standardized and controlled clinical vocabulary was developed as a boundary object in the domain of a complex and chronic health condition, namely, multiple chemical sensitivity, to improve communication among multidisciplinary clinicians. A convenience sample of 100 patients with a diagnosis of multiple chemical sensitivity, nine multidisciplinary clinicians involved in the care of patients with multiple chemical sensitivity, and 36 clinicians in the community participated in the study. RESULTS: Eighty-two percent of the multidisciplinary and inconsistent vocabulary was standardized using the Systematized Nomenclature of Medicine - Clinical Terms (SNOMED(®) CT as a reference terminology. Over 80% of the multidisciplinary clinicians agreed on the overall usefulness of having a controlled vocabulary as a boundary object. Over 65% of clinicians in the community agreed on the overall usefulness of the vocabulary. CONCLUSION: The results from this study are promising and will be further evaluated in the domain of another complex chronic condition, ie, chronic pain. The study was conducted as a preliminary analysis for developing a boundary object in a heterogeneous domain of knowledge.
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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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| 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".