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Record W2791188264 · doi:10.1111/cid.12585

Preservation and augmentation of molar extraction sites affected by severe bone defect due to advanced periodontitis: A prospective clinical trial

2018· article· en· W2791188264 on OpenAlexvenueno aff
Liping Zhao, Tao Xu, Wenjie Hu, Kwok‐Hung Chung

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2018
Typearticle
Languageen
FieldDentistry
TopicDental Radiography and Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsMolarBuccal administrationSoft tissueDentistryExtraction (chemistry)MedicineRidgeResorptionPeriodontitisBone resorptionOrthodonticsSurgeryChemistryPathologyInternal medicineBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Studies of the extracted infected-molar ridge preservation are limited. PURPOSE: To compare alterations of hard and soft tissue in infected-molar sockets receiving ridge preservation compared with natural healing. MATERIALS AND METHODS: Thirty-five infected-molar extraction sites either preserving with Bio-Gide membrane covered the Bio-Oss material or receiving natural healing procedure as controls. The soft tissue profile was evaluated before tooth extraction and after 6-month healing. Cone-beam computed tomography scans were taken immediately and 6 months after extraction. Vertical and horizontal bone changes were assessed radiographically. Data were analyzed with Mann-Whitney U test and α = 0.05. RESULTS: No significant differences in soft tissue and vertical bone changes in the medium region of the sockets were found (P > .05). Buccal bone changes in the mesial and distal sites in the test group were significantly lower than the control group (P < .05). Ridge width increased from 0.21mm to 5.30mm at 1mm apical from the crest in the test and reduced from 0.12 mm to 1.00 mm in the control groups. CONCLUSION: Ridge preservation at periodontally compromised molar extraction sites might compensate for ridge width and buccal bone resorption that occurs with natural healing alone.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.080
GPT teacher head0.469
Teacher spread0.390 · 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

Citations50
Published2018
Admission routes1
Has abstractyes

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