Incidence and trends of central line associated pneumothorax using radiograph report text search versus administrative database codes
Bibliographic record
Abstract
BACKGROUND: Central line associated pneumothorax (CLAP) could be a good quality of care indicator because they are objectively measured, clearly undesirable and possibly avoidable. We measured the incidence and trends of CLAP using radiograph report text search with manual review and compared them with measures using routinely collected health administrative data. METHODS: For each hospitalisation to a tertiary care teaching hospital between 2002 and 2015, we searched all chest radiography reports for a central line with a sensitive computer algorithm. Screen positive reports were manually reviewed to confirm central lines. The index and subsequent chest radiography reports were screened for pneumothorax followed by manual confirmation. Diagnostic and procedural codes were used to identify CLAP in administrative data. RESULTS: In 685 044 hospitalisations, 10 819 underwent central line insertion (1.6%) with CLAP occurring 181 times (1.7%). CLAP risk did not change over time. Codes for CLAP were inaccurate (sensitivity 13.8%, positive predictive value 6.6%). However, overall code-based CLAP risk (1.8%) was almost identical to actual values possibly because patient strata with inflated CLAP risk were balanced by more common strata having underestimated CLAP risk. Code-based methods inflated central line incidence 2.2 times and erroneously concluded that CLAP risk decreased significantly over time. CONCLUSIONS: Using valid methods, CLAP incidence was similar to those in the literature but has not changed over time. Although administrative database codes for CLAP were very inaccurate, they generated CLAP risks very similar to actual values because of offsetting errors. In contrast to those from radiograph report text search with manual review, CLAP trends decreased significantly using administrative data. Hospital CLAP risk should not be measured using administrative data.
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".