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Record W2790662053 · doi:10.1002/jso.24960

Comparison of open and closed abdomen techniques for the delivery of intraperitoneal pemetrexed using a murine model

2018· article· en· W2790662053 on OpenAlexaff
David Badrudin, Lucas Sidéris, Camille Perrault‐Mercier, Julien Hubert, François A. Leblond, Pierre Dubé

Bibliographic record

VenueJournal of Surgical Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicIntraperitoneal and Appendiceal Malignancies
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsPemetrexedMedicinePharmacokineticsAbdomenArea under the curveChemotherapyPerfusionAntifolatePharmacologySurgeryInternal medicineAntimetabolite

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Pemetrexed is an appealing agent to use for cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (HIPEC). However, the optimal method of pemetrexed delivery still remains undefined. Using a murine model, we compared the use of open and closed abdomen techniques on the absorption of intraperitoneal (IP) pemetrexed in different compartments. METHODS: ) at a perfusion temperature of 40°C during 25 min according to two techniques: open and closed. At the end of perfusion, samples in different compartments were harvested and the concentrations of pemetrexed were measured by high performance liquid chromatography. RESULTS: Absorption of IP pemetrexed in portal and systemic blood was significantly higher using the open compared to the closed abdomen technique (93.17 vs 52.50 µg/mL, P < 0.001) and (76.26 vs 51.65 µg/mL, P < 0.001), respectively. No difference was found between the two techniques on the peritoneal tissue concentration of pemetrexed (18.07 vs 19.17 µg/g, P = 0.51). CONCLUSION: Peritoneal absorption of pemetrexed is not modified by the use of either technique. However, systemic concentrations of pemetrexed increased using the open technique, suggesting it could increase systemic toxicity.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.100
GPT teacher head0.435
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2018
Admission routes1
Has abstractyes

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