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Record W2198722430

An index for the measurement of normal maximum mouth opening

2003· article· en· W2198722430 on OpenAlexvenueno aff
خالد زواوي

Bibliographic record

VenueJournal of The Canadian Dental Association · 2003
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDistal interphalangeal jointAnatomyPosition (finance)OrthodonticsMiddle fingerRing fingerDentistryBiology
DOInot available

Abstract

fetched live from OpenAlex

PURPOSE: The aim of this study was to evaluate the relationship between the width of 3 or 4 fingers of one hand and maximum mouth opening (MMO) in healthy subjects. METHODS: One hundred and forty dental students (age 21 to 42 years, mean 27.4 years) participated in the study. The ability of each subject to position 3 or 4 fingers, vertically aligned, between the upper and lower central incisors up to the first distal interphalangeal folds, was documented. Measurements of MMO and the width of 3 fingers (index, middle and ring fingers) and 4 fingers (index, middle, ring and little fingers) were recorded. RESULTS: All subjects were able to position 3 fingers (of both the right and left hands) between the upper and lower central incisors. Only 12 subjects were able to position 4 fingers (both right and left) in this way. There were no significant differences among the measurements of MMO (mean 48.8 mm), 3 fingers of the right hand (mean 47.3 mm) and 3 fingers of the left hand (mean 47.0 mm) (p > 0.05). However, MMO was significantly different from the width of 4 fingers of the right hand (mean 58.1 mm) and 4 fingers of the left hand (mean 57.5 mm) (p < 0.001). Moreover, there was a strong positive correlation between MMO and the 3-finger measurements (p < 0.0001). CONCLUSIONS: These findings strongly suggest that the ability to position 3 fingers in the mouth during dental examination is a convenient index for assessing normal MMO

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)0.003
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 score0.044
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 integrity0.0000.000
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 head0.018
GPT teacher head0.250
Teacher spread0.232 · 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
Published2003
Admission routes1
Has abstractyes

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