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A Compact Dual-Mode Twisting Retraction Device for Endoscopic Submucosal Dissection

2025· article· W4416750796 on OpenAlexaff
Haley Mayer, Eran Shlomovitz, James M. Drake, Thomas Looi, Eric Diller

Bibliographic record

Venuenot available
Typearticle
Language
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsUniversity of TorontoUniversity Health NetworkHospital for Sick Children
Fundersnot available
KeywordsTraction (geology)Endoscopic submucosal dissectionString (physics)Tractive forceDissection (medical)Clutch

Abstract

fetched live from OpenAlex

Endoscopic submucosal dissection (ESD) is a technically difficult, minimally invasive, organ preserving resection technique that yields improved clinical outcomes when compared to current conventional procedures but requires experienced surgeons and specialized skills. Difficulty in applying tension during ESD is recognized as the single greatest barrier to wide adoption of the procedure, and solution of this problem is sure to have wide-reaching and immediate adoption. This work presents a compact wireless retraction device that is magnetically actuated and has a high force output with adaptable traction control. The retraction device is 25 mm long and 4 mm in diameter. The device has two modes of operation: first spooling to collect string slack, then transitions via external permanent magnet to internal string twisting to generate a large retraction force. In slack collection the device can contract 11 cm in length at a speed of 6.88 millimeters per second, then clutch to force mode to reach a peak retraction force of 1.33 N, leveraging the micro-transmission twisted string actuation. The wireless device is designed for endoscopic deployment to any surgical environment or lesion within the gastrointestinal tract.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.031
GPT teacher head0.362
Teacher spread0.331 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
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

Citations0
Published2025
Admission routes1
Has abstractyes

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