Systematic review of grading systems for adverse surgical outcomes
Bibliographic record
Abstract
Background: Grading scales for adverse surgical outcomes have been poorly characterized to date. The primary aim of this study was to conduct a systematic review to enumerate the various frameworks for grading adverse postoperative outcomes; our secondary objective was to outline the properties of each grading system, identifying its strengths and weaknesses. Methods: We searched 9 databases (Africa Wide Information, Biosis, Cochrane, Embase, Global Health, LILACs, Medline, PubMed and Web of Science) from 1992 (the year the Clavien-Dindo classification system was developed) until Mar. 2, 2017, for studies that aimed to develop or improve on an already existing generalizable system for grading adverse postoperative outcomes. Study selection was duplicated as per PRISMA recommendations. Procedure-specific grading systems were excluded. We assessed the framework, strengths and weaknesses of the systems qualitatively. Results: We identified 9 studies on 8 adverse outcome grading systems with frameworks generalizable to any surgical procedure. Most systems have not been widely incorporated in the literature. Seven of the 8 systems were produced without including patients' perspectives. Four allowed the derivation of a composite morbidity score, which had limited tangible significance for patients. Conclusion: Although each instrument identified offered its own advantages, none satisfied the need for a patient-centred tool capable of generating a composite score of all possible postoperative adverse outcomes (complications, sequelae and failure) that enables comparison of noninterventional and surgical management of disease. There is a need for development of a more comprehensive, patient-centred grading system for adverse postoperative outcomes.
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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 | 0.059 | 0.233 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.036 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".