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Record W2129520988 · doi:10.1177/1756283x08103341

Effective treatment of chronic radiation proctitis using radiofrequency ablation

2009· article· en· W2129520988 on OpenAlexfundno aff
Chao Zhou, Desmond C. Adler, Laren Becker, Yu Chen, Tsung-Han Tsai, Marisa Figueiredo, Joseph M. Schmitt, James G. Fujimoto, Hiroshi Mashimo

Bibliographic record

VenueTherapeutic Advances in Gastroenterology · 2009
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsnot available
FundersNational Cancer InstituteNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthAir Force Office of Scientific ResearchNational Science Council
KeywordsMedicineArgon plasma coagulationRadiation proctitisRadiofrequency ablationAblationAblative caseEsophagusProctitisSubmucosaEndoscopySurgeryRadiologyHemostasisCoagulative necrosisRadiation therapyInternal medicine

Abstract

fetched live from OpenAlex

Endoscopic argon plasma coagulation and bipolar electrocautery are currently preferred treatments for chronic radiation proctitis, but ulcerations and strictures frequently occur. Radiofrequency ablation (RFA) has been successful for mucosal ablation in the esophagus. Here we report the efficacy of RFA with the BarRx Halo(90) system in three patients with bleeding from chronic radiation proctitis. In all cases, the procedure was well tolerated and hemostasis was achieved after 1 or 2 RFA sessions. Re-epithelialization of squamous mucosa was observed over areas of prior hemorrhage. No stricturing or ulceration was seen on follow-up up to 19 months after RFA treatment. Real-time endoscopic optical coherence tomography (EOCT) visualized epithelialization and subsurface tissue microvasculature pre- and post-treatment, demonstrating its potential for follow-up assessment of endoscopic therapies.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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

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 head0.008
GPT teacher head0.272
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), 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

Citations83
Published2009
Admission routes1
Has abstractyes

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Same venueTherapeutic Advances in GastroenterologySame topicOptical Coherence Tomography ApplicationsFrench-language works237,207