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Record W2889022630 · doi:10.5946/ce.2018.072

Effect of Sending Educational Video Clips via Smartphone Mobile Messenger on Bowel Preparation before Colonoscopy

2018· article· en· W2889022630 on OpenAlexaboutno aff
Sung Chan Jeon, Jae Hyun Kim, Sun Jung Kim, Hye Jung Kwon, Youn Jung Choi, Kyoungwon Jung, Sung Eun Kim, Won Moon, Moo In Park, Seun Ja Park

Bibliographic record

VenueClinical Endoscopy · 2018
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCLIPSColonoscopyBowel preparationInternal medicineSurgeryColorectal cancer

Abstract

fetched live from OpenAlex

BACKGROUND/AIM: We aimed to evaluate the efficacy of sending educational video clips via smartphone mobile messenger (SMM) on enhancing bowel preparation before colonoscopy. METHODS: This was a prospective, endoscopist-blinded, randomized controlled study. Patients in the SMM group received two video clips sent via SMM that explained the diet and regimen for bowel preparation, whereas those in the control group did not receive any video clips. We compared the quality of bowel preparation between the two groups, which was assessed by an endoscopist using the Ottawa scale. RESULTS: Between August and November 2014, 140 patients in the SMM group and 141 patients in the control group underwent colonoscopic examination. The total Ottawa score of the SMM group was significantly lower than that of the control group (5.47±1.74 vs. 5.97±1.78, p=0.018). These results were particularly prominent in the younger age group; the total Ottawa score of patents in the SMM group aged <40 years was significantly lower than that of patients in the control group aged <40 years (5.10±1.55 vs. 6.22±2.33, p=0.034). CONCLUSION: We demonstrated that sending educational video clips via SMM could result in better bowel preparation, especially in the younger age group.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.019
GPT teacher head0.407
Teacher spread0.388 · 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

Citations43
Published2018
Admission routes1
Has abstractyes

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