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Can Telehealth Technology Be an Important Factor in Providing High Quality Colonoscopy? A Proof of Concept Study

2018· article· en· W2920915670 on OpenAlexaboutno aff
Thi Khuc, Christian Jackson, Daniel Chao, Ralph Clark

Bibliographic record

VenueThe American Journal of Gastroenterology · 2018
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsnot available
Fundersnot available
KeywordsColonoscopyMedicineTelehealthAttendanceInternal medicineColorectal cancerHealth careGeneral surgeryTelemedicineCancer

Abstract

fetched live from OpenAlex

Introduction: The VA Loma Linda Healthcare System (VALLHCS) offers telehealth technology healthcare services to patients who live closer to outlying VA designated Community Based Outpatient Clinics (CBOCS) than VALLHCS. Anecdotally, we noticed a significant number of cancellations and no-shows for colonoscopy from patients who sought care from CBOCS. We developed a telehealth based colonoscopy class (TBCC) to be given at CBOCS. To determine the usefulness of TBCC, we aimed to show non-inferiority between TBCC and our existing in-person colonoscopy class (IPCC) at VALLHCS, in terms of quality of colonoscopy, class attendance rates, and colonoscopy appointment compliance rates. Adenoma detection rate (ADR), polyp detection rate (PDR) and adenocarcinoma detection rate (ACDR) was analyzed as quality indicators of adequate colonoscopy. Methods: We retrospectively reviewed charts for 101 patients enrolled in IPCC and 101 patients enrolled in TBCC between April 2014 and September 2014. Based on a modified Ottawa bowel preparation score, all patients had either good or fair bowel prep. The primary end points were ADR, PDR and ACDR. Secondary endpoints were attendance to TBCC and IPCC, and attendance to colonoscopy. Attendance rate was defined as the percentage of patients who complied with their initial scheduled appointment. Chi-square analysis was used to calculate relative risks (RR), confidence intervals (CI) and p-values. Results: The ADR for TBCC and IPCC was 53.5% and 54.5% (RR 0.98, 95% CI 0.76 to 1.27, p-value 0.89). The PDR for TBCC and IPCC was 31.7% and 33.7% (RR 0.94, 95% CI 0.63 to 1.39, p-value 0.76). Adenocarcinoma was identified in 7 TBCC colonoscopies and 1 IPCC colonoscopy (RR 7.00, 95% CI 0.87 to 55.87, p-value 0.07). TBCC and IPCC attendance rates were 91.1% and 88.1%, respectively. Patients were more likely to attend TBCC than IPCC; however, this was not statistically significant (OR 1.03, 95% CI 0.94 to 1.14, p-value 0.49). The colonoscopy attendance rates for TBCC and IPCC were 91.1% and 89.1% (OR 1.02, 95% CI 0.93 to 1.12, p-value 0.64). Conclusion: TBCC was non-inferior to IPCC for ADR, PDR, class attendance and colonoscopy attendance. A TBCC program may be a means of increasing patient attendance to colonoscopy preparation training, which subsequently can increase compliance with colorectal cancer screening.

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)0.021
metaresearch head score (Gemma)0.021
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 score0.021
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.027
GPT teacher head0.342
Teacher spread0.315 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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