MétaCan
Menu
Back to cohort
Record W2110954347 · doi:10.1177/154405910408300310

Speech with Maxillary Implant Prostheses: Ratings of Articulation

2004· article· en· W2110954347 on OpenAlexaff
Guido Heydecke, David H. McFarland, Jocelyne S. Feine, James P. Lund

Bibliographic record

VenueJournal of Dental Research · 2004
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsUniversité de MontréalMcGill University
FundersMedical Research CouncilDeutsche Forschungsgemeinschaft
KeywordsProsthesisArticulation (sociology)DentistryImplantOrthodonticsMedicineManner of articulationDental prosthesisAudiologySurgery

Abstract

fetched live from OpenAlex

Speech is often perturbed after placement of maxillary implant-retained prostheses. We tested the hypothesis that the rate of speech errors varies with prosthetic design. Thirty edentulous subjects with mandibular implant prostheses entered two within-subject crossover trials. Subjects wore maxillary fixed prostheses and removable long-bar overdentures (Trial 1), or overdentures with and without palates (Trial 2). Test words from a French language speech battery were recorded after each prosthesis had been worn for two months. The percentages of stops, fricatives, and vowels correctly perceived by lay judges were calculated. Subjects produced a significantly higher percentage of sounds correctly with overdentures than with fixed prostheses. Between-treatment differences were significant for stops and fricatives (p < 0.01), but not for vowels. There were no significant differences in error rates between the two overdentures. In conclusion, maxillary implant overdentures with and without palates enable patients to produce more intelligible speech than fixed prostheses.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.059
GPT teacher head0.396
Teacher spread0.337 · 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 source (direct Gemma or distilled Codex), 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

Citations74
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Dental ResearchSame topicDental Implant Techniques and OutcomesFrench-language works237,207