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Record W4306292611 · doi:10.53350/pjmhs22169198

Causative Factors of Tooth Wear among Patients Visiting a Tertiary Care Hospital in Lahore

2022· article· en· W4306292611 on OpenAlexaff
Qudsia Iqbal, Muhammad Waseem Ullah Khan, Muhammad Sohaib Nawaz, Hamna Khawaja, Momina Akram, Hafiz Nasir Mahmood

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDentistry
TopicDental Erosion and Treatment
Canadian institutionsCollège Montmorency
Fundersnot available
KeywordsTooth wearMedicineDentistryAbrasion (mechanical)AttritionEtiologyTooth lossTooth ErosionPathologicalTooth surfaceOral healthInternal medicineEnamel paint

Abstract

fetched live from OpenAlex

Background: Tooth wear, or as it is also often referred to as non-carious tooth surface loss (TSL), can be described simply as ‘the pathological non-carious loss of tooth tissue’. Tooth wear is often multifactorial in nature, making clinical diagnosis difficult. Identification of the etiology is essential for the successful management of the pathology. Methods: A total of 120 patients of both male and female with tooth wear were selected from dental OPD. Patients with age group 25-65years with tooth wear in at least two teeth according to basic erosive wear examination (BEWE) were included. Questionnaire covering primary risk factors that might cause tooth wear was used Data was analyzed using SPSS version 24.0. Results: Out of the 120 tooth wear patients, 69(57%) were male and 51(43%) were females. tooth wear presented more in males as compared to females. 42.24% patients reported with habit of tea consumption and 32.5% with cold drinks. 45% males and 43.4% females had gastric reflex disease. 60.9% male patients had problem of bruxism and clenching. Conclusion: This study reported that TSL has a multifactorial etiology. Parafunction, gastro esophageal reflux disease (GORD) and consumption of carbonated drinks were the most commonly observed causative factors. Keywords: Tooth wear, Erosion, Abrasion, Attrition, Causative factor

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.234
Teacher spread0.228 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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