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Record W4361195616 · doi:10.1055/a-2009-2196

Combining endoscopic mucosal resection with hybrid argon plasma coagulation to reduce local colorectal lesion recurrence: a video tutorial

2023· article· en· W4361195616 on OpenAlexaff
Melissa Zarandi‐Nowroozi, Daniel von Renteln

Bibliographic record

VenueEndoscopy · 2023
Typearticle
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsArgon plasma coagulationMedicineEndoscopic mucosal resectionPerforationAblationSurgeryAblative caseLesionResectionEndoscopyRadiation therapyInternal medicine

Abstract

fetched live from OpenAlex

Endoscopic mucosal resection (EMR) is a safe, effective, and surgery-sparing technique for removing large colorectal lesions [1]. Although piecemeal EMR is recommended for polyps > 20 mm in size, it remains a suboptimal technique for complete lesion resection, with high rates of residual polyp and recurrence rates reaching 15 % [2]. Hybrid argon plasma coagulation (hAPC) is a novel approach allowing ablation of resection margins and surface after EMR. hAPC combines waterjet injection and argon plasma coagulation (APC) in a single device. On-demand, repeatable saline cushioning without instrument exchange reduces thermal ablative insult to deep tissue structures, allowing for effective destruction of micro-remnants with reduced risk of perforation. High technical success and low recurrence rates (0 %) were shown in a pilot study using EMR in combination with hAPC [3]. A prospective international multicenter study showed that the local recurrence rate was only 2.2 % when using hAPC after EMR [4]. One recent meta-analysis found the local recurrence rate to be 3.3 % after hot EMR and margin ablation at 12-month follow-up [5].

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.028
GPT teacher head0.311
Teacher spread0.283 · 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 designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

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