An Algorithm to Assess Guideline Concordance of Antibiotic Choice in Community-Acquired Pneumonia
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: This study aimed to develop and evaluate an algorithm to reduce the chart review burden of improvement efforts by automatically labeling antibiotic selection as either guideline-concordant or -discordant based on electronic health record data for patients with community-acquired pneumonia (CAP). METHODS: We developed a 3-part algorithm using structured and unstructured data to assess adherence to an institutional CAP clinical practice guideline. The algorithm was applied to retrospective data for patients seen with CAP from 2017 to 2019 at a tertiary children's hospital. Performance metrics included positive predictive value (precision), sensitivity (recall), and F1 score (harmonized mean), with macro-weighted averages. Two physician reviewers independently assigned "actual" labels based on manual chart review. RESULTS: Of 1345 patients with CAP, 893 were included in the training cohort and 452 in the validation cohort. Overall, the model correctly labeled 435 of 452 (96%) patients. Of the 286 patients who met guideline inclusion criteria, 193 (68%) were labeled as having received guideline-concordant antibiotics, 48 (17%) were labeled as likely in a scenario in which deviation from the clinical practice guideline was appropriate, and 45 (16%) were given the final label of "possibly discordant, needs review." The sensitivity was 0.96, the positive predictive value was 0.97, and the F1 was 0.96. CONCLUSIONS: An automated algorithm that uses structured and unstructured electronic health record data can accurately assess the guideline concordance of antibiotic selection for CAP. This tool has the potential to improve the efficiency of improvement efforts by reducing the manual chart review needed for quality measurement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".