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Record W2586499774 · doi:10.1093/ecco-jcc/jjx002.576

P451 Compound IBD patient profiles are related to specific information needs – an Israeli national survey

2017· article· en· W2586499774 on OpenAlexaff
Saleh Daher, Ariel A. Benson, John R. Walker, Oded Hammerman, Tawfik Khoury, Timna Naftali, Rami Eliakim, Ofer Ben‐Bassat, Çharles N. Bernstein, Eran Israeli

Bibliographic record

VenueJournal of Crohn s and Colitis · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineExploratory factor analysisInformation needsDiseaseCoping (psychology)Patient-Reported Outcomes Measurement Information SystemUlcerative colitisFamily medicinePhysical therapyClinical psychologyInternal medicinePsychometrics

Abstract

fetched live from OpenAlex

Background: Recent studies show that a majority of IBD patients receive insufficient information regarding their disease. The wide range of knowledge needs necessitates resources that may improve patient adherence and outcomes. The aims of this nationwide survey of IBD patients were to relate specific patient profiles to unique information needs, with the intent of constructing an interactive, patient tailored, knowledge resource. Methods: Patients from the Israeli Crohn's & Colitis Foundation were asked to complete a questionnaire aimed at rating the amount of information received at time of diagnosis, the importance of information to be delivered for newly diagnosed patients, and the current importance of information topics for patients with longstanding disease. We performed an exploratory factor analysis of the baseline questionnaire responses, and analyzed the responses of a predetermined set of 20 patient profiles generated by the study team Results: A total of 571 patients (52% males) completed the questionnaire, 382 with Crohn's disease, 179 with ulcerative colitis and 10 with IBD-Undefined. Little information was received at disease onset, with a low rating spanning the whole questionnaire (average rating of 0.9 out of 5). The rating of importance of information topics averaged 4.2/5, thus reflecting unmet needs. Average rating of current information needs was also rated high (average 3.5/5), implying continued relevance to the experienced patient. Factor analysis for current needs grouped the responses into six knowledge domains (factors), namely: therapy & complications; nutrition; stress & coping; social & religion; work & disability; and long term complications, all rated as highly important (6/8), except for social & religion (3.2/8). Analysis of the data by single questions or by knowledge domains did not yield specific associations to demographic and clinical characteristics. Analysis by patient profiles found significant, clinically relevant associations to specific knowledge domains. For instance, patients newly diagnosed at an age>50 and patients diagnosed more than 10 years ago showed less interest in information regarding work & disability, while patients with active disease showed more interest in information regarding work & disability, stress & coping and therapy & complications. Patients in remission but on mesalamine or no therapy showed less interest in all factors except for nutrition and long term complications Conclusions: Our study clearly demonstrates unmet patient information needs, spanning all knowledge domains. Analysis per compound patient profiles revealed unique associations to information topics, and paves the way to building a patient tailored information resource

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.248
GPT teacher head0.393
Teacher spread0.145 · 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 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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