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Record W2340323771 · doi:10.1155/2016/2139264

Age Is the Only Predictor of Poor Bowel Preparation in the Hospitalized Patient

2016· article· en· W2340323771 on OpenAlexaff
Julia McNabb‐Baltar, Alastair Dorreen, Hisham Al Dhahab, Michael Fein, Xin Xiong, Mike Byrne, Imene Ait, Myriam Martel, Alan Barkun

Bibliographic record

VenueCanadian Journal of Gastroenterology and Hepatology · 2016
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineColonoscopyLogistic regressionBowel preparationInternal medicineOdds ratioIntubationMultivariate analysisGastroenterologySurgeryColorectal cancerCancer

Abstract

fetched live from OpenAlex

We examine the impact of key variables on the likelihood of inpatient poor bowel preparation for colonoscopy. Records of inpatients that underwent colonoscopy at our institution between January 2010 and December 2011 were retrospectively extracted. Univariable and multivariable logistic regression models were fitted to assess the effect of clinical variables on the odds of poor preparation. Tested predictors included age; gender; use of narcotics; heavy medication burden; comorbidities; history of previous abdominal surgery; neurological disorder; product used for bowel preparation, whether or not the bowel regimen was given as split or standard dose; and time of endoscopy. Overall, 244 patients were assessed including 83 (34.0%, 95% CI: 28.1-39.9%) with poor bowel preparation. Cecal intubation was achieved in 81.1% of patients (95% CI: 76.2-86.0%). When stratified by quality of bowel preparation, cecal intubation was achieved in only 65.9% (95% CI: 60.0-71.9%) of patients with poor bowel preparation and 89.9% (95% CI: 86.1-93.7%) of patient with good bowel preparation. In multivariate logistic regression analysis, only advancing age was an independent predictor of poor bowel preparation (OR = 1.026, CI: 1.006 to 1.045, and p = 0.008). Age is the only independent predictor of poor bowel preparation amongst hospitalized patients.

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 score0.061
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.235
Teacher spread0.226 · 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

Citations19
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Gastroenterology and HepatologySame topicColorectal Cancer Screening and DetectionFrench-language works237,207