Validation of the Enhanced Recovery After Surgery (ERAS) database in Alberta, Canada
Bibliographic record
Abstract
ABSTRACT Background The Enhanced Recovery After Surgery (ERAS) Interactive Audit System (EIAS) is a retrospectively collected database containing information about the preoperative, intraoperative, and postoperative components of surgical patient care. EIAS was created to allow centers that have adopted ERAS protocols to assess their performance. To have confidence in the data collected by EIAS, its completeness, accuracy and validity must be assessed. This study aims to assess the validity of the Alberta EIAS when compared to the gold standard measurement for patient data, the patient electronic medical record (EMR). Methods Four sites that implemented ERAS across Alberta were included, with 20 to 60 patient EMRs pulled from each site. Data on fourteen pre-specified variables was abstracted from patient EMRs and compared to the corresponding variables from EIAS. Validation criteria included (I) accuracy (agreement between EMR and EIAS) and (II) missingness (percent of data that was missing in patients EMR and EIAS). The estimates of accuracy were compared to estimates of accuracy from two other EIAS validation studies using meta-analysis. Results A total of 113 patient charts were reviewed across four sites. Agreement between chart review and EIAS was 73.6% (59.9% - 87.3%) with a mean sensitivity of 70.3 and mean specificity of 50.1. Agreement between chart review and EIAS was better among outcomes (agreement for re-operation was 93.7%) than it was for accuracy of documentation of the ERAS elements (mean agreement=73.6%). Agreement varied by site (68.5% to 94.4%) and reviewer (68.0% to 96.6%). Across all fifteen ERAS elements, a mean of 11.4% of data was missing, with re-operation having the greatest proportion of missing data (15.9%) and termination of drains and early mobilization with the lowest proportion of missing data (9.7%). Estimates of accuracy were not different between studies (I 2 =56.4%, p=0.101). Conclusions In Alberta, EIAS is an accurate and complete source of data suggesting that EIAS is a valid and reliable source of data to explore patient outcomes and adoption of ERAS guidelines. This study found that data abstractors that are medically trained, and trained in standardized data abstraction are important determinants of generating high quality data, highlighting the need for adequate resources for data collection.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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".