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Record W113924306

[Application of computer-controlled local anesthetic delivery system in children].

2011· article· zh· W113924306 on OpenAlexaboutno aff
Xin Zhao, Hongling Liu, Man Qin

Bibliographic record

VenuePubMed · 2011
Typearticle
Languagezh
FieldDentistry
TopicDental Anxiety and Anesthesia Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMolarLocal anesthesiaBuccal administrationSyringeAnesthesiaLocal anestheticDentistryVisual analogue scaleAnesthetic
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare the reaction of children receiving computer-controlled local anesthetic delivery system (CCLADS) and a conventional syringe, and evaluate the efficacy of anesthesia and the reaction after treatment. METHODS: 30 children aged 4 to 9 years old, whose bilateral primary molars in the same jaw present similar lesions with similar treatment were included in the study. These bilateral primary molars were treated under local anesthesia in 2 separate visits with random use of either the CCLADS or conventional syringe. For the first visit, the first group received CCLADS anesthesia, in which buccal infiltration anesthesia was performed for maxillary primary molars, and periodontal ligament anesthesia was performed for mandibular primary molars. The second group received conventional anesthesia, in which buccal infiltration anesthesia was performed for both maxillary and mandibular primary molars. For the second visit, two groups used alternative anesthestic method. Pain behavior was observed and scored by modified-children's hospital of eastern ontario pain scale (MCHEOPS). Pain perception was rated using facial image scale (FIS). RESULTS: The MCHEOPS and FIS scores in 1 min injection of children with CCLADS were lower than children with conventional syringe (P<0.05). No difference was found in the FIS score in treatment between two injectors (P>0.05). CONCLUSION: In local anesthesia, CCLADS can reduce the injection pain caused by conventional syringe to a certain extent.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.189
Teacher spread0.178 · 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.

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

Citations7
Published2011
Admission routes1
Has abstractyes

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