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Record W2079845507 · doi:10.1159/000067034

Influence of the Platelet-Activating Factor Receptor Antagonist BB-882 on Intra-Abdominal Adhesion Formation in Rats

2003· article· en· W2079845507 on OpenAlexaff
Selçuk Otçu, H. Öztürk, M. Aldemir, Ali İhsan Dokucu

Bibliographic record

VenueEuropean Surgical Research · 2003
Typearticle
Languageen
FieldMedicine
TopicIntestinal and Peritoneal Adhesions
Canadian institutionsPediatric Oncology Group
Fundersnot available
KeywordsMedicineCecumAdhesionAntagonistPlatelet-activating factorReceptor antagonistSalinePlatelet-activating factor receptorPeritoneumAbdominal cavityAbdominal wallInternal medicineReceptorUrologyGastroenterologySurgeryChemistry

Abstract

fetched live from OpenAlex

Postoperative intra-abdominal adhesion formation is a major clinical problem. We aimed to examine the preventive effect of treatment with the platelet-activating factor (PAF) antagonist (lexipafant, BB-882) on experimentally induced intra-abdominal adhesion formation in rats. Twenty male Sprague-Dawley rats weighing 250 and 290 g were studied. Generation of adhesions in rats by brushing a 1-cm(2) area of the cecum and the peritoneum on the right side of the abdominal wall was followed by intra-abdominal administration of saline and 5 mg/kg in a volume of 0.2 ml PAF receptor antagonist BB-882. After 45 days, formation of adhesions was graded and histological evaluation was processed. The severity of adhesions was significantly less in the BB-882 group than in the control group (p < 0.001, p < 0.05). The average adhesion scores in the control and BB-882 groups were 3.2 +/- 0.6 and 0.6 +/- 0.6, respectively, and the difference between both groups was found to be significant (p < 0.0001). The number of polymorphonuclear leukocytes and fibrotic areas was significantly decreased in the BB-882 group when compared to the control group (p < 0.001, p < 0.002). In conclusion, this study confirms the efficacy of BB-882 in the prevention of postoperative intra-abdominal adhesions in a rat model.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.130
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.093
GPT teacher head0.370
Teacher spread0.277 · 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

Citations6
Published2003
Admission routes1
Has abstractyes

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