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[Standards and guidelines of radiation protection and safety in dental X-ray examinations].

2017· review· en· W2981943882 on OpenAlexaboutno aff
Xiaolong Guo, Gang Li, Yong Cheng, Qin Yu, Z Y Zhang

Bibliographic record

VenuePubMed · 2017
Typereview
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsGuidelineMedicineMainland ChinaRadiation protectionMedical physicsSafety standardsChinaFamily medicineDentistryNuclear medicinePathologyPolitical science

Abstract

fetched live from OpenAlex

With the rapid development of imaging technology, the application of dental imaging in diagnosis, treatment planning, intraoperative surgical navigation, monitoring of treatment or lesion development and assessment of treatment outcomes is playing an essential role in oral healthcare. The increased total number of dental X-ray examinations is accompanied by a relatively significant increase in collective dose to patients as well as to dental healthcare workers, which is harmful to human bodies to a certain degree. Some radiation protection standards and guidelines in dental radiology have been published in European countries, US, Canada and Australia, etc. Adherence to these standards and guidelines helps to achieve images with diagnostic quality and avoid unnecessary and repeated exposures. However, no radiation protection standard or guideline with regard to dental X-ray examinations has been put in force so far in mainland China. Therefore, a literature review on available radiation protection standards and guidelines was conducted to provide reference to the development of radiation protection standards or guidelines in mainland China.

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)0.011
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.005
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0040.001
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0070.005

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 head0.121
GPT teacher head0.385
Teacher spread0.263 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2017
Admission routes1
Has abstractyes

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