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Record W4389799083 · doi:10.1922/ejprd_2631nayar10

Do Implant- Supported/Retained Prostheses Improve the Quality of Life of Patients with Intraoral Maxillofacial Defects? – A Systematic Review

2023· article· en· W4389799083 on OpenAlexaff
Sandeep Krishan Nayar

Bibliographic record

VenueEuropean Journal of Prosthodontics and Restorative Dentistry · 2023
Typearticle
Languageen
FieldMedicine
TopicReconstructive Facial Surgery Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineDentistrySystematic reviewImplantQuality of life (healthcare)Quality assessmentMEDLINESurgeryNursingPathology

Abstract

fetched live from OpenAlex

BACKGROUND: There is limited evidence available regarding patient satisfaction and quality of life assessment in patients with intraoral maxillofacial defects managed with maxillofacial prostheses. OBJECTIVES: This systematic review aims to understand the impact of intraoral implant prostheses in improving the quality of life in patients with intraoral maxillofacial defects/abnormalities. METHODS: A comprehensive search was performed of nine electronic databases from January 1970 to August 2022. Hand searching of the reference lists of the included papers and of relevant journal publications between 2012 and 2022 was also undertaken. Key information was extracted from included studies alongside quality and risk of bias assessments. RESULTS: The systematic review encompassed a total of seven studies, comprising five retrospective and two prospective investigations, with one of the prospective studies being a randomised clinical trial. The evaluation of the risk of bias and quality assessment revealed heterogeneity in the results, preventing meaningful comparisons among the included studies. CONCLUSION: Within the limitation of the systematic review, there is limited evidence to suggest that implant prostheses improve the quality of life in patients with intraoral maxillofacial defects or abnormalities.

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)0.002
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 score0.079
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
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 science0.0000.000
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 head0.036
GPT teacher head0.312
Teacher spread0.277 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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Same venueEuropean Journal of Prosthodontics and Restorative DentistrySame topicReconstructive Facial Surgery TechniquesFrench-language works237,207