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Record W4400832573 · doi:10.1038/s41415-024-7610-6

Clinical outcomes of short dental implants supporting prostheses in the posterior region

2024· article· en· W4400832573 on OpenAlexaff
Murtaza Hirani, Hannah Arnantha, Azza Al-Mossallami, George Paolinelis

Bibliographic record

VenueBDJ · 2024
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicineOsseointegrationPremolarImplantDentistryMaxillaMandible (arthropod mouthpart)Survival rateOrthodonticsSurgeryMolar

Abstract

fetched live from OpenAlex

Aim In clinically challenging scenarios with limited bone height and anatomical restrictions, the use of short implants have been proposed as a potential treatment strategy. The purpose of this retrospective study was to evaluate the clinical outcomes of short implants supporting prostheses in the posterior premolar region of the maxilla and mandible.Materials and methods A total of 30 patients requiring short 6 mm length implant placement in the posterior premolar region were included. Following a period of osseointegration, the implants were restored with either single crowns, fixed bridges or implant-supported removable overdentures. Implant and prosthetic survival with technical complications were recorded.Results In total, 45 implants were placed, with four failures reported before loading in two patients, resulting in a patient implant survival rate of 93.3% over the two-year follow-up. There was no statistically significant difference found between implant failure and arch placement. Prosthetic survival was 100% and minor technical complications recorded were low.Conclusion This study showed that short 6 mm implants could provide a viable treatment option, with high survival rates comparable with alternative bone augmentation procedures. Further research with longer observation periods would be required to validate these current findings.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.078
GPT teacher head0.425
Teacher spread0.347 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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