The use of ROTEM in Detection of Coagulopathy and Altered Hemostasis in Patients Undergoing Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy
Bibliographic record
Abstract
BACKGROUND: Patients undergoing Cytoreductive Surgery (CRS) and Hyperthermic Intraperitoneal Chemotherapy (HIPEC) are at risk of coagulopathy. This study aims to evaluate the potential role of rotational thromboelastometry (ROTEM) in detecting alterations in coagulation during and after CRS/HIPEC. METHODS: A prospective observational study was conducted at a single tertiary care center. All consecutive patients undergoing CRS/HIPEC from April 2021 to December 2022 were enrolled. Participants were monitored using ROTEM, INR, PTT, and Fibrinogen at four time points (pre-incision, post-HIPEC, and on postoperative days 1 and 3). RESULTS: A total of 35 patients were included. Significant changes were observed from pre-incision to post-HIPEC coagulation parameters: mean fibrinogen decreased from 3.5 g/L to 2.1 g/L and mean INR increased from 1.1 to 2.1, p < 0.05. By postoperative day 3, all parameters had recovered to their pre-incision baselines, with EXTEM ML30 and fibrinogen significantly increased from baseline. Lower pre-incision fibrinogen was significantly associated with increased intra-operative blood loss, p < 0.05. Anesthesiologists reported that intra-operative ROTEM influenced management in 17% of cases (5/30). CONCLUSIONS: CRS/HIPEC is associated with significant changes in the coagulation profile that largely normalize by postoperative day 3. Utilizing ROTEM intraoperatively can help identify patients at risk of intra-operative bleeding and guide transfusion strategies.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".