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Failure rates of miniscrews inserted in the maxillary tuberosity

2019· article· en· W2983252308 on OpenAlexaff
Muhammad Azeem, Arfan Ul Haq, Zubair Hassan Awaisi, Muhammad Mudassar Saleem, Muhammad Waheed Tahir, Ahmad Liaquat

Bibliographic record

VenueDental Press Journal of Orthodontics · 2019
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsCollège Montmorency
Fundersnot available
KeywordsMedicineLogistic regressionDentistryFailure rateOrthodonticsOdds ratioInternal medicineMathematics

Abstract

fetched live from OpenAlex

INTRODUCTION: Anchorage conservation in orthodontics has always been a challenge. OBJECTIVE: The aim of this current study was to find out the failure rate of miniscrews inserted in the maxillary tuberosity (MT) region. METHODS: This pilot study consisted of 40 patients (23 female, 17 male; mean age = 20.1±8.9 years) that had received 60 MT miniscrews for orthodontic treatment. Clinical notes and pictures were used to find out the primary outcome of miniscrew failure. Independent failure factors were also investigated. Logistic regression analysis was done for predictor's relation with MT miniscrews failure. RESULTS: There was no significant correlation in failure rate according to various predictor variables, except for miniscrews installed by lesser experienced operators, which showed significantly more failure. The odds ratio for miniscrew failure placed by inexperienced operators was 4.16. CONCLUSION: A 26.3% failure rate of mini-implants inserted in the MT region was observed.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.032
GPT teacher head0.304
Teacher spread0.271 · 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

Citations13
Published2019
Admission routes1
Has abstractyes

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