Letter to the editor: enhancing healthcare-associated infection reporting in Canada
Bibliographic record
Abstract
To the Editor, Healthcare-associated infections (HAI) are the most frequently reported adverse event in healthcare worldwide.1 In Ontario and Quebec, Canada, C. difficile (CDI) and methicillin-resistant S. aureus (MRSA) HAI surveillance are mandatory for acute care hospitals.2,3 Traditional active surveillance by infection prevention and control (IPC) teams is considered the "gold-standard" but is time-consuming and labor-intensive.3 However, passive surveillance using administrative data would be a cost-effective alternative as it relies on routinely collected and consistently defined data.4 The Canadian Institute for Health Information (CIHI) implemented HAI surveillance through its Discharge Abstract Database (DAD), an administrative database that contains a codified summary of a patients' stay in hospital, which is coded by the hospital clinical coding team (CCT) using the International Classification of Diseases 10th Revision (ICD-10) codes, in accordance with CIHI standards.5 However, it has been demonstrated that its accuracy in Canada is limited in comparison with active surveillance data.6 Since 2016, the CCT at St. Joseph's Healthcare Hamilton (SJHH), Ontario, has used IPC data to report CDI and MRSA infections to CIHI.To do so, IPC sent a monthly HAI list to the CCT, ensuring alignment and provided training on the differences and definitions used for these infections.This practice deviates from CIHI standards, which mandate that coders only use chart documentation from physicians, midwives, or nurse practitioners.In 2017, SJHH transitioned to an electronic medical record (EMR), allowing IPC team to document infections directly, eliminating the need for monthly lists.A report was developed to support monthly and year-end reconciliation, enhancing data accuracy.The collaborative approach was readily adopted, resolving data discrepancies.As a quality assessment project approved by the Director of professional Services, we conducted a study to assess the validity of
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.023 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".