Validation of ICD-10 codes for studying foreign body airway obstructions: A health administrative data cohort study
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Bibliographic record
Abstract
Aim: To validate a case definition for foreign body airway obstructions (FBAO) using International Classification of Diseases version 10 (ICD-10) codes to accurately identify patients in administrative health databases and improve reporting on this injury. Methods: We identified prehospital patient encounters in Alberta, Canada between Jan 1, 2018 and Dec 31, 2021 by querying the provincial emergency medical services' (EMS) patient care records for FBAO-related presentations, EMS protocols, or treatments. We deterministically linked EMS patient encounters to data on emergency department visits and hospital admissions, which included ICD-10 codes. Two physicians independently reviewed encounters to determine true FBAO cases. We then calculated diagnostic accuracy measures (sensitivity, specificity, likelihood ratios) of various algorithms. Results: We identified 3677 EMS patient encounters, 2121 were linked to hospital administrative databases. Of these encounters, 825 (38.9%) were true FBAO. The combination of two ICD-10 codes (T17 = foreign body in the respiratory tract or T18.0 = foreign body in the mouth) was the most specific algorithm (96.9% [95%CI 95.8-97.8%]), while the combination of all FBAO-related ICD-10 codes and R06.8 (other breathing abnormalities) was the most sensitive (75.0% [95%CI 71.9-78.0]). We identified an additional 453 (35.4%) FBAO cases not transported by EMS (due to death or transport refusal), and therefore not linked to the hospital administrative databases. Of these unlinked encounters, 44 (9.7%) cases resulted in the patient's death. Conclusions: FBAO can be identified with reasonable accuracy using health administrative data and ICD-10 codes. All algorithms had a trade-off between sensitivity and specificity, and failed to identify a third of FBAO cases, of which 10% resulted in death.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it