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Record W4295154832 · doi:10.53730/ijhs.v6ns4.12134

Assessment of informational needs in Behcet’s patients

2022· article· en· W4295154832 on OpenAlexaboutno aff
Ola Ibrahim Abdo Elmetwaly, Salwa Hagag Abdelaziz, Lobna A. Maged

Bibliographic record

VenueInternational Journal of Health Sciences · 2022
Typearticle
Languageen
FieldMedicine
TopicOcular Diseases and Behçet’s Syndrome
Canadian institutionsnot available
Fundersnot available
KeywordsPsychosocialArabicBehcet's diseaseMedicineDiseaseDescriptive researchSample (material)GerontologyFamily medicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Behçet’s disease (BD) is one of the rare rheumatologic diseases which affects most commonly young adults in the third or fourth decade of life. Inspite of the multi system nature of the disease can lead to temporary or permanent functional disability, informational needs of BD patients is still unknown as no studies has investigated such issue, hence the aim is to assess the informational needs among patients with Behçet’s disease. A cross-sectional descriptive design on a convenient sample of 68 adult BD patients was utilized. Data was collected using the following tools: (a) structured interview questionnaire and (b) the Arabic version of Toronto Informational Needs Questionnaire (TINQ) adapted for BD. The highest percentage of the study sample was male (82.40%) came from rural area (61.80%) and aged 31-42 (58.00%) with a mean age of (38±7.01 years). The study results showed the information related to treatment, disease characteristics, investigative tests and psychosocial items are highly important needs among BD patients. Age, gender and education are influencing factors for informational needs in the sample of the study. Further replication of the study is recommended from different geographical areas.

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)0.000
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 score0.012
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.024
GPT teacher head0.387
Teacher spread0.363 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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