Validation of ICD-9 diagnostic codes for bronchopulmonary dysplasia in Quebec's provincial health care databases
Bibliographic record
Abstract
INTRODUCTION: Bronchopulmonary dysplasia (BPD) is a chronic respiratory disease caused by neonatal lung injury. The aim of this study was to validate the use of ICD-9 diagnostic codes for BPD in administrative databases to allow for their use in health care utilization analyses. METHODS: The validation process used a retrospective cohort composed of preterm infants, with or without respiratory complications, admitted to the Montréal Children's Hospital, Montréal, Quebec, between 1983 and 1992. BPD subjects were identified using ICD-9 diagnostic codes in the provincial administrative databases (medical services and MED-ECHO) and then matched with subjects with confirmed BPD from the validation cohort. We examined concordance and estimated sensitivity and specificity associated with the use of these diagnostic codes for BPD. RESULTS: True positive and false negative BPD subjects did not differ significantly according to gestational age, birth weight and Apgar scores. False positive BPD subjects were found to have significantly lower gestational age than true negative subjects. The use of the ICD-9 diagnostic codes for BPD was associated with a specificity between 97.6% and 98.0%. The sensitivity was lower at 45.0% and 52.4% for the medical services and MED-ECHO databases, respectively. Milder cases of BPD tended to be missed more frequently than more severe cases. CONCLUSION: The specificity of the use of ICD-9 diagnostic codes for BPD in the Quebec provincial health care databases is adequate to allow its routine use. Its lower sensitivity for milder cases will likely result in an underestimation of the impacts of BPD on the long-term health care utilization of preterm infants.
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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 | 0.012 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".