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Bone level changes proximal to oral implants supporting fixed partial prostheses

2002· article· en· W2127266651 on OpenAlexaff
Chris Wyatt, George A. Zarb

Bibliographic record

VenueClinical Oral Implants Research · 2002
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsDentistryRadiographyMedicineImplantProsthesisBone remodelingBone densityOrthodonticsSurgeryOsteoporosis

Abstract

fetched live from OpenAlex

The success of oral implant treatment relies on the presence and maintenance of bone adjacent to implants. The monitoring of radiographic bone level changes provides valuable insight into the longevity of oral implants. The purpose of this study was to measure radiographic bone level changes proximal (mesial and distal) to Brånemark System) implants (Nobel Biocare AB, Göteborg, Sweden) supporting fixed partial prostheses. Measurements were used to determine mean bone loss for the first year of loading by the prosthesis and the mean annual bone loss for subsequent years. These results were then compared and contrasted with various characteristics of the individuals, treatment, and treatment outcomes. Fifty-five subjects with 69 fixed partial prostheses supported by 160 implants were followed over a 1 to 12-year period. A mean bone loss of 0.33 mm (SD 0.59) was measured for the first year of loading and a mean annual bone loss of 0.00 mm (SD 0.11) after the first year. The radiographic bone loss calculated for implants at the first year of loading was positively correlated with the mean annual bone loss thereafter. Males, younger individuals and those implants supporting distal extension prostheses lost significantly more bone in the first year of loading. Larger numbers of implants followed for longer periods of time are needed to further explore the effects of various aspects of treatment on bone loss.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.009

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 head0.582
GPT teacher head0.557
Teacher spread0.025 · 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; both teacher heads agree on what is shown here.

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

Citations57
Published2002
Admission routes1
Has abstractyes

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