Variable impact of complications in general surgery: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Registering complications is important in surgery, since complications serve as outcome measures and indicators of quality of care. Few studies have addressed the variation in severity and consequences of complications. We hypothesized that complications show much variation in consequences and severity. METHODS: We conducted a prospective observational cohort study to evaluate consequences and severity of complications in surgical practice. All recorded complications of patients admitted to our hospital between June 1, 2005, and Dec. 31, 2007, were prospectively recorded in an electronic database. Complications were classified according to the system of the Trauma Registry of the American College of Surgeons. We graded the severity of complications according to the system proposed by Clavien and colleagues, and the consequences of each complication were registered. RESULTS: During the study period, 3418 complications were recorded; consequences and severity were recorded in 89% of them. Of 3026 complications, 987 (33%) were grade I, 781 (26%) were grade IIa, 1020 (34%) were grade IIb, 150 (5%) were grade III and 88 (3%) were grade IV. The consequences and severity of identically registered complications showed a large degree of variation, best illustrated by wound infections, which were grade I in 50%, grade IIa in 22%, grade IIb in 28% and grade III and IV in 0.3% of patients. CONCLUSION: Severity should be routinely presented when reporting complications in clinical practice and surgical research papers to adequately compare quality of care and results of clinical trials.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".