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Record W2414984416 · doi:10.2478/aoj-2007-0009

The impact of orthodontic treatment on normative need. A case-control study in Peru

2007· article· en· W2414984416 on OpenAlexaff
Eduardo Bernabé, Socorro Aída Borges-Yáñez, Carlos Flores‐Mir

Bibliographic record

VenueAustralasian Orthodontic Journal · 2007
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNormativeControl (management)MedicineNormative social influencePsychologyDentistryPolitical scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the impact of previously provided orthodontic treatment on the normative need in a sample of young adult Peruvians. METHODS: Six hundred and thirty five freshmen, representative of all first year students registering in 2002 at a private university in Lima, were randomly screened to obtain 63 cases and 126 controls. A case was defined as having a definite orthodontic treatment need determined by the DAI and IOTN indices simultaneously. A control was defined as having no need of orthodontic treatment based on both indices. Students were also asked if they had previously undergone any orthodontic treatment. Binary logistic regression was used for the statistical analysis. RESULTS: Sex, age and socioeconomic status of the students were not statistically associated with normative orthodontic treatment need (p = 0.258, 0.556 and > or = 0.272 respectively). The percentage of students with a previous history of orthodontic treatment was similar between the cases and the controls (14.3 per cent and 11 .9 per cent respectively). There were no statistically significant associations between the variables. CONCLUSIONS: The impact of previously provided orthodontic treatment on the current normative need of young adults was limited. Properly designed studies are required to assess the reasons for these findings.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.032
GPT teacher head0.355
Teacher spread0.323 · 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.

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

Citations4
Published2007
Admission routes1
Has abstractyes

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