Morbidity, toxicity, and mortality classification systems in the local regional treatment of peritoneal surface malignancy
Bibliographic record
Abstract
To reach a consensus for reporting complications related to cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC). Reporting the adverse events related to CRS + HIPEC is not standardized yet. Post-operative complications can be divided in two categories: the effects of surgical manipulation per se and the toxic effects of the heated intraoperative chemotherapy. Additive and/or synergistic effects also exist. Different centers have published their experience with regard to the complications associated with the procedure. Various classification systems have been used which makes a temptative comparison of the different techniques and results almost impossible. An effort was made here to review the existing major classification systems: The Bozzetti classification, the Clavien classification (and two proposed modifications from Feldman et al. and Elias et al.) and the Common terminology criteria for adverse events (CTCAE) version 3.0 of the National Institute of Health (NIH) criteria. A related document was sent to an international panel of experts. The CTCAE was adopted by the panel of experts as the unique classification system to be used for reporting complications related to CRS + HIPEC.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".