External Validation of ASPECT (Algorithm for Suspected Pulmonary Embolism Confirmation and Treatment)
Bibliographic record
Abstract
BACKGROUND: Administrative health care databases are increasingly being used to study pulmonary embolism (PE), but the validity of single PE codes is variable. Using data from Quebec, Canada, we developed ASPECT (Algorithm for Suspected Pulmonary Embolism Confirmation and Treatment), combining 3 components to ascertain confirmed PE: emergency department (ED) diagnoses, imaging codes, and dispensed prescriptions or hospital diagnoses. Herein, we used unrelated administrative health care databases to externally validate ASPECT. METHODS: We used ED electronic health records (ED-EHRs) to identify all residents of Calgary (Alberta, Canada) with PE codes between January and June, 2016. We applied ASPECT by identifying imaging studies in the ED-EHR, admission diagnoses in linked discharge abstract database and filled prescriptions in linked pharmacy information. Confirmed PE in ASPECT was validated against chart review in the ED-EHR. RESULTS: The cohort included 498 patients. Overall, 258 (51.9%) were managed as outpatients and 327 were adjudicated to have confirmed PE; the positive predictive value (PV) of single PE codes was 65.6%. With ASPECT the positive PV was 96.5% [95% confidence interval (CI), 94.4-98.5%] and positive likelihood ratio was 10.9 (95% CI, 6.8-15.1). The negative PV and negative likelihood ratio were 85.1% (95% CI, 80.0-90.2%) and 0.1 (95% CI, 0.0-0.1), respectively. Overall agreement of ASPECT with confirmed PE was 92.2%. Further, ASPECT was similarly robust in inpatients and outpatients and was more precise than any 2-component combination of ASPECT. CONCLUSIONS: Our findings reiterate the limitations of using single administrative codes for PE and suggest ASPECT as an acceptable tool to study PE.
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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) | 0.001 | 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 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".