Rationale for Heating Oxaliplatin for the Intraperitoneal Treatment of Peritoneal Carcinomatosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To study the effect of heat on the absorption of intraperitoneal (IP) oxaliplatin using a murine model. BACKGROUND: Because of its efficiency in the systemic treatment of colorectal cancer, oxaliplatin is currently used in hyperthermic intraperitoneal chemotherapy (HIPEC) for peritoneal carcinomatosis. However, its properties when administered by the IP route have not been well characterized by preclinical studies. METHODS: Under general anesthesia, 35 Sprague-Dawley rats were submitted to 3 different doses of IP oxaliplatin (460, 920, and 1840 mg/m(2)) at 3 different perfusion temperatures (37, 40, and 43°C) during 25 minutes. At the end of perfusion, samples in different compartments (peritoneum, portal blood, and systemic blood) were harvested and the concentrations of oxaliplatin were measured by high performance liquid chromatography. RESULTS: As the dose of IP oxaliplatin was increased, higher concentrations were observed in every compartment. When the temperature of IP oxaliplatin was increased, it resulted in an increase of its peritoneal concentration (linear regression 0.38; 95% CI: 0.28-0.47) and in a decrease of its systemic blood (linear regression -1, 02; 95% CI: -1.45 to -0.60) and portal blood (linear regression -1.08; 95% CI: -1.70 to -0.47) concentrations. CONCLUSION: Proportionally to the dose administered, IP oxaliplatin leads to high concentration of drug in peritoneal tissues. Furthermore, heat enhances peritoneal tissue concentration of Oxaliplatin while reducing its systemic absorption. This last effect may possibly lead to decreased systemic toxicity. These observations support the use of oxaliplatin for 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it