Evaluation of an electronic health record structured discharge summary to provide real time adverse event reporting in thoracic surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The reporting of adverse events (AE) remains an important part of quality improvement in thoracic surgery. The best methodology for AE reporting in surgery is unclear. An AE reporting system using an electronic discharge summary with embedded data collection fields, specifying surgical procedure and complications, was developed. The data are automatically transferred daily to a web-based reporting system. METHODS: We determined the accuracy and sustainability of this electronic real time data collection system (ERD) by comparing the completeness of record capture on procedures and complications with coded discharge data (administrative data), and with the standard of chart audit at two intervals. All surgical procedures performed for 2 consecutive months at initiation (Ti) and 1 year later (T1yr) were audited by an objective trained abstractor. A second abstractor audited 10% of the charts. RESULTS: The ERD captured 71/72 (99%) of charts at Ti and 56/65 (86%) at T1yr. Comparing the presence/absence of complications between ERD and chart audit demonstrated at Ti a high sensitivity and specificity, positive predictive value (PPV) of 95.5%, negative predictive value (NPV) of 93.9% with a kappa of 0.872 (95% CI 0.750 to 0.994), and at T1yr a sensitivity, specificity, PPV and NPV of 100% with a kappa of 1.0 (95% CI 1.0). Comparing the presence/absence of complications between administrative data and chart audit at Ti demonstrated a low sensitivity, high specificity and a kappa of 0.471 (95% CI 0.256 to 0.686), and at T1yr a low sensitivity, high specificity of 85% and a kappa of 0.479 (95% CI 0.245 to 0.714). CONCLUSIONS: We found that the ERD can provide accurate real time AE reporting in thoracic surgery, has advantages over previous reporting methodologies and is an alternative system for surgical clinical teams developing AE reporting systems.
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.
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 | 0.118 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 it