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High Age at the Time of Implant Installation is Correlated with Increased Loss of Osseointegrated Implants in the Temporal Bone

2007· article· en· W2031696402 on OpenAlexvenueno aff
Vassilis Drinias, Gösta Granström, Anders Tjellström

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2007
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsnot available
Fundersnot available
KeywordsOsseointegrationMedicineImplantDentistryImplant failureSurvival rateBlood flowSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The implant failure rate in temporal bone has been reported to be about 5 to 10% over a 10-year period. A number of our elderly patients have shown increased failure rates over a long time period, which is the reason for the present study. PURPOSE: The aim of the present study was to find out if age is correlated with implant failure and to measure blood flow in implant sites. MATERIALS AND METHODS: The long-time survival of 131 osseointegrated implants installed in the temporal bones of 81 patients was correlated with the age of the patient at the time of installation. The blood flow in 37 fixture installation sites in 22 patients was recorded by means of laser Doppler flowmetry. RESULTS: The mean implant failure rate in the study group was 9.8% after a mean follow-up time of 7.6 years. There was a significant increase of implant failure in patients above 60 years of age. There was further a trend that implants used for the bone-anchored hearing aid were lost to a higher proportion than implants used for bone-anchored episthesis. There was also a trend that female patients lost fewer implants than males. Blood flow in the temporal bone correlated well with the age of the patient in that the highest values were recorded from the youngest patients. CONCLUSIONS: Increasing age affects failures of osseointegrated implants in the temporal bone. Blood flow is higher in the child's temporal bone, a factor that can be of importance to understand why age influences implant survival.

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)0.008
metaresearch head score (Gemma)0.001
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 score0.101
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.062
GPT teacher head0.397
Teacher spread0.335 · 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

Citations30
Published2007
Admission routes1
Has abstractyes

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