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Record W2897253736 · doi:10.3126/ojn.v8i1.21341

Dentoskeletal Changes in Class II Subjects following Treatment with Twin Block and Herbst Appliance

2018· article· en· W2897253736 on OpenAlexaff
Khurram Shahzad, Javeeria Asif Cheema, Muhammad Azeem, Waheed Ul Hamid

Bibliographic record

VenueOrthodontic Journal of Nepal · 2018
Typearticle
Languageen
FieldHealth Professions
TopicDental Education, Practice, Research
Canadian institutionsCollège Montmorency
Fundersnot available
KeywordsMedicineSignificant differenceDentistryMean valueMean differenceBlock (permutation group theory)OrthodonticsMathematicsInternal medicineStatisticsGeometry

Abstract

fetched live from OpenAlex

Objective: To compare the mean changes in dentoskeletal parameters in Class II patients treated by Twin Block versus Herbst appliance.Materials & Method: The study was conducted at the Orthodontic Department of Children’s Hospital and Institute of Child Health and de’Montmorency College of Dentistry, Lahore. The study involved 50 patients those were randomized in equal numbers according to lottery method to either Group-1 (Twin block) or Group-2 (Herbst). Mean changes in SNA, SNB and IMPA at the end of treatment was calculated by subtracting Pretreatment measurements (T1) from post treatment measurements (T2). Student t–test was used to compare the mean changes in dentoskeletal parameters in both groups.Result: The comparison showed that the mean difference recorded in SNA values was -1.06±0.62 in Group-1 and -1.28±0.61 in Group-2 (p-Value 0.07), SNB was 2.14±0.70 in Group-1 and 1.22±0.55 in Group-2, (p-Value 0.001) while IMPA was 1.58±0.64 in Group-1 and 4.8±1.31 in Group-2 (p-Value 0.001).Conclusion: There was a significant difference between mean changes in dentoskeletal parameters in Class II patients treated by twin block when compared to Herbst appliance.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.049
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.067
GPT teacher head0.447
Teacher spread0.380 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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