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A Study of Dental Implants in Medically Treated Hypothyroid Patients

2002· article· en· W2066323105 on OpenAlexaffvenue
Nikolai Attard, George A. Zarb

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2002
Typearticle
Languageen
FieldMedicine
TopicThyroid Disorders and Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineContraindicationImplantDentistryDental implantProsthesisEdentulismMedical historyOsseointegrationImplant failureDental prosthesisSoft tissueSurgery

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to investigate the success outcomes of implants and prosthodontic treatment placed in patients with a previous history of hypothyroidism that was being controlled with medications. MATERIALS AND METHODS: Twenty-seven female patients with a medically confirmed history of primary hypothyroid disease who were on replacement medications at the time of implant surgery were selected as the study group. They were matched with 29 control patients by age, gender, location (jaw and zone) of implants, type of prosthesis, and dental status of the opposing arch. Additional factors studied were medical history, medications, smoking habits, and bone quality and quantity. RESULTS: There was no statistical difference in the number of implant failures between the two groups (p = .781). The hypothyroid patients had more soft tissue complications (p = .018) following stage 1 surgery. More bone loss around implants in the hypothyroid patients was recorded after year 1 of loading when compared with loss in their matched controls (p = .017). CONCLUSIONS: This study suggests that medically controlled hypothyroid female patients treated with dental implants are not at higher risk of implant failure when compared with matched controls, and that a history of controlled hypothyroidism does not appear to be a contraindication for implant therapy with endosseous implants.

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)0.001
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 score0.023
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.137
GPT teacher head0.443
Teacher spread0.306 · 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

Citations50
Published2002
Admission routes2
Has abstractyes

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