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Investigation of transmigrated mandibular canines

2019· article· en· W3003125292 on OpenAlexaff
Muhammad Azeem, Ambreen Afzal, Zubair Ahmed, Munawar Manzoor Ali, Arfan Ul Haq, Waheed Ul Hamid

Bibliographic record

VenueDental Press Journal of Orthodontics · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicdental development and anomalies
Canadian institutionsCollège Montmorency
Fundersnot available
KeywordsMandibular canineMedicineDentistryOrthodonticsRadiographyPopulationSurgery

Abstract

fetched live from OpenAlex

INTRODUCTION: Canine transmigration is a rare orthodontic condition and it is relevant to perform the proper diagnose at an early age. OBJECTIVE: The aim of the current study was to find out the frequency of transmigrated mandibular canines (TRC) in orthodontic patients obtained from South Asian population of Pakistan origin. METHODS: Panoramic radiographs of 2,550 untreated orthodontic patients (1,248 males; 1,302 females) were included, to investigate the presence, site, and type of TRC. Any permanent mandibular canine that was found to be crossing the midline in panoramic radiographs was considered as TRC. RESULTS: The frequency of TRC was found to be 0.98%. TRC were only found unilaterally. No significance regarding gender and side was found. Nineteen TRC displayed a type 1 transmigratory pattern, while type 2 and type 5 transmigratory patterns were encountered in three patients. CONCLUSION: Frequency of transmigrated mandibular canines in the studied sample was 0.98%.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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

Citations15
Published2019
Admission routes1
Has abstractyes

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