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Record W3214695208 · doi:10.36106/ijsr/6831141

PHYSICIAN KNOWLEDGE, ATTITUDE AND PRACTICE TOWARDS FIBROMYALGIAAND FACTOR'S INFLUENCING KNOWLEDGE IN EASTERN PROVINCE , SAUDI ARABIA

2021· article· en· W3214695208 on OpenAlexaboutno aff
Hanan AlObaid, Laila AlRashed, Ayat AlHraiz, Erum Khalid, Mai AlDhamadi

Bibliographic record

VenueINTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH · 2021
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFamily medicineQuarter (Canadian coin)DiseaseMedical knowledgeMedical education

Abstract

fetched live from OpenAlex

Introduction: Fibromyalgia is a common rheumatological disease that is difcult to diagnose , because of it's subjective symptoms and decient physician's knowledge of the disease . The purpose of this study is to assess knowledge, attitude and practice of family physicians regarding bromyalgia Methods: The study was a cross sectional with an online survey that was administered to general practitioners and family physicians in Eastern province, responses were obtained from 209 participants, knowledge about bromyalgia, attitude and practice were assessed Results: Regarding knowledge about bromyalgia, 89% of the respondents had poor knowledge. Attitudes revealed that More than half of the respondents (56%) thought that bromyalgia is easy to diagnosed. However, only 12.4% of the medical practitioners agreed that diagnosis of bromyalgia can be conducted in PHC setting and only 16.7% felt condent to use (ACR) criteria. Regarding management, about 27% believed they can manage bromyalgia . Only a quarter (24.9%) said they never manage any cases of bromyalgia. Conclusions: knowledge, attitude and practice regarding bromyalgia are poor which results in delay in the diagnosis and management.To overcome this delays in diagnosis and treatment and improve knowledge about bromyalgia, further education is needed

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.091
GPT teacher head0.434
Teacher spread0.343 · 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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueINTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCHSame topicFibromyalgia and Chronic Fatigue Syndrome ResearchFrench-language works237,207