Development and validation of an algorithm of diagnostic and procedural codes for the identification of children hospitalized with a tracheostomy in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The requirement for a tracheostomy in children is associated with significant morbidity, mortality, and healthcare utilization. Easy identification of children with tracheostomies would facilitate important research on this population and provide quality improvement initiatives. AIM: The purpose of this study is to determine whether an algorithm of diagnostic and procedural codes can accurately identify children hospitalized with a tracheostomy using routinely collected health data. METHODS: Chart reviews were performed at the Children's Hospital of Eastern Ontario (CHEO) and the London Health Sciences Center (LHSC) to establish a true positive cohort of pediatric patients with tracheostomies admitted between 2008 and 2016. A multidisciplinary team developed algorithms of diagnostic and procedural codes contained within the Canadian Institute for Health Information Discharge Abstract Database. Algorithms were tested and refined against the true-positive and true-negative cohort. The accuracy of the diagnostic codes related to tracheostomy complications was also evaluated. RESULTS: A chart review identified 158 unique children with tracheostomies (77 at CHEO, 81 at LHSC) with 901 individual admissions (401 at CHEO, 507 at LHSC). The best algorithms for identifying children with a tracheostomy had a sensitivity and specificity of more than 99%, a positive predictive value (PPV) of 94.0% and negative predictive value (NPV) of 100%. The algorithm for the identification of tracheostomy-related complications had a sensitivity of 76.7%, a specificity of 65%, PPV of 52.3%, and an NPV of 84.7%. CONCLUSIONS: This study provides an algorithm for the accurate identification of children hospitalized in Canada with a tracheostomy, facilitating population-level epidemiological research and quality improvement initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".