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Record W2884900003 · doi:10.5539/jmbr.v8n1p114

Knowledge and Practice of General Dental Practitioners Concerning Dental Trauma Management in Children in Ahvaz, Iran

2018· article· en· W2884900003 on OpenAlexvenueno aff
Leyla Basir, Mohsen Shayesteh, Mahsa Atiyeh Heydari

Bibliographic record

VenueJournal of Molecular Biology Research · 2018
Typearticle
Languageen
FieldHealth Professions
TopicDental Trauma and Treatments
Canadian institutionsnot available
FundersAhvaz Jundishapur University of Medical Sciences
KeywordsMedicineDental practiceDental traumaFamily medicineKnowledge retentionDentistryMedical education

Abstract

fetched live from OpenAlex

Background and Objectives: Traumatic dental injuries (TDIs) are unpleasant experiences for children and they necessitate to be treated as soon as possible. This cross-sectional study aimed to assess the knowledge and practice of general dental practitioners (GDPs) regarding emergency management of TDIs in Ahvaz, Iran.Subjects and Methods: In this study, a two-part questionnaire was responded by 100 GDPs. The first section included questions on demographic information and the second section was composed of questions on different dental Injuries. One score was assigned to each correct answer; the total score of 10 to 30 was considered as low knowledge and practice, while scores 30-50, 50-70 and above 70 were considered as moderate, good, and high levels of knowledge and practice, respectively. The data were analyzed using Pearson’s Correlation, t-test and regression.Results: With regards to the level of GDP’s knowledge, the mean score was 59.2%. A total of 100 (51%) dentists showed a good level of knowledge. A significant association was found between knowledge and practice of GDPs in their practice encountering and treating TDI (P=0.001).Conclusion: The overall knowledge of GDPs about management of TDI in the selected community was good.

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)0.003
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 score0.017
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
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 head0.145
GPT teacher head0.549
Teacher spread0.404 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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