Microorganisms Linked to Health Care–Associated Infections: Modernization of Terminology Resources for Reporting to the National Healthcare Safety Network
Bibliographic record
Abstract
Unlabelled: The National Healthcare Safety Network (NHSN) of the Centers for Disease Control and Prevention (CDC) needed a modernized approach to manage resources containing standardized terminology that specify microorganism data submitted electronically for legacy reporting. Health care-associated infections (HAIs) reported to NHSN require the submission of data regarding specific microorganisms attributed to the patient's condition. Data on microorganisms submitted to the NHSN electronically must use the SNOMED CT terminology standard. Terminology artifacts that guide submission of microorganism data have been maintained in spreadsheets that have become increasingly challenging to manage. This case report details the initial use case for the implementation of off-the-shelf software within the NHSN to modernize the maintenance of terminology assets. Resources that guide reporting microorganisms for HAIs were used as a prototype to demonstrate how a software application can be practically implemented to streamline the maintenance of complex terminology assets. Mission-critical artifacts have been reconciled and consolidated into a single source of truth knowledgebase using an off-the-shelf software solution. This report shares progress and lessons learned regarding the modernization of NHSN's Pathogen Codes resource and its derivative artifacts. A model is now available that can be replicated across other NHSN legacy artifacts. Our experience can be applied to other public health use cases and information systems facing similar challenges with attachments to legacy terminology resources and systems.
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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 | 0.029 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".