Inter-rater Reliability of ACS-NSQIP Colorectal Procedure Coding in Canada
Bibliographic record
Abstract
Abstract The American College of Surgeons National Surgical Quality Improvement Project (ACS-NSQIP) collects risk-adjusted surgical data utilizing Current Procedural Terminology (CPT) codes. Inconsistent code assignment may affect benchmarking calculations. This study aims to assess inter-rater reliability of coding colorectal resection procedures across Canada by ACS-NSQIP surgical clinical nurse reviewers (SCNR) and the impact on risk predictions. An electronic survey was distributed to Canadian SCNRs, asking them to assign CPT codes to simulated synoptic operative reports. Percent agreement and free-marginal kappa correlation were calculated. The ACS-NSQIP risk calculator was used to compare predicted morbidity and mortality between the two most frequently chosen codes for each case, to demonstrate impact on risk prediction. 44 of 150 (29.3%) survey recipients responded. There was significant variability in the CPT codes chosen. Agreement ranged from 6.7% 62.3%. Free-marginal kappa correlation ranged from moderate agreement (0.53) to high disagreement (-0.17). The ACS-NSQIP risk calculator predicted absolute differences in risk of serious complications and mortality ranging from 0.2–13.7% and 0.2–6.3%, respectively. This study demonstrated low inter-rater reliability in the coding of ACS-NSQIP colorectal resection procedures in Canada among trained SCNRs. The resulting coding inconsistency translated to variation in risk prediction.
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 | 0.029 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".