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Record W4212835599 · doi:10.1093/jcag/gwab049.168

A169 <i>PRICE:</i> PREVENTING READMISSIONS IN IBD CENTRES OF EXCELLENCE

2022· article· en· W4212835599 on OpenAlexaff
Frances Dang, Peter Habashi, Zane Gallinger, Geoffrey C. Nguyen

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2022
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineRandomized controlled trialAmbulatoryTransitional carePopulationHealth careIntervention (counseling)Emergency medicineAmbulatory carePhysical therapyInternal medicineNursing

Abstract

fetched live from OpenAlex

Abstract Background Hospital readmission rates are high in the inflammatory bowel disease (IBD) population, with 20% of patients readmitted within the same year. Discharge processes are not routinely standardized and deficiencies in transition of care puts patients at an increased risk of recurrent illness and healthcare costs. In addition, hospitalizations for IBD patients are associated with nosocomial complications such as venous thromboembolism. Aims We hypothesize that standardized follow-up by an IBD practice nurse and electronic health outcome monitoring reduces the risk of hospital readmission compared to current approaches of hospital discharge alone. Methods This pilot study uses a prospective parallel randomized control design and includes patients admitted with an IBD flare who were discharged without surgical intervention. Patients randomized to the control arm were discharged with usual standard of care (i.e. discharge summary and/or follow-up). In addition to standard of care, those in the intervention group received organized telephone or email follow-up by an IBD practice nurse at 1, 7 and 30 days post-discharge. These patients also received bi-weekly corespondence from an electronic survey tool, NoviSurvey, to determine clinical disease severity and medication adherence. Based on patient interactions and survey responses, the IBD nurse may arrange for expedited ambulatory visit or readmission for high-risk patients. Results At present, 41 patients have been enrolled into our study, 4 of which were excluded due to surgical management. 19 patients were randomized to the intervention and 17 to the control group. In the intervention group, the 30-day cumulative rate of readmission [0/19 (0%) vs. 4/17 (24%), p = 0.040] as well as the proportion of patients who failed to taper steroids [0/18 (0%) vs. 5/15 (33%), p = 0.013] was significantly lower when compared to the control group. There were no occurrences of deep vein thrombosis within 30 days post-discharge in either group. Conclusions These findings in our pilot study indicate that a nurse led post-discharge intervention may translate to benefits including decreased readmission rates and better medication adherence, warranting a large clinical trial to confirm findings. Patient Demographics and Clinical Outcomes Between Intervention and Control Groups Funding Agencies CAG, CCC

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)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.386
Threshold uncertainty score0.876

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.001
Research integrity0.0060.003
Insufficient payload (model declined to judge)0.3860.158

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 head0.025
GPT teacher head0.325
Teacher spread0.300 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

Explore more

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