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Record W4308620764 · doi:10.37762/jgmds.9-4.315

The Role of Lead Toxicity on Eruption Rate of Hypofunctional Incisors in Albino Wistar Rats

2022· article· en· W4308620764 on OpenAlexaff
Rashid Javaid, Asaad Javaid Mirza, Asrar Ahmed, Aqeel Ibrahim, Qayyum Akhtar, Ruqayya Sana, Faiz Rasul

Bibliographic record

VenueJournal of Gandhara Medical and Dental Science · 2022
Typearticle
Languageen
FieldHealth Professions
TopicOral and gingival health research
Canadian institutionsCollège Montmorency
Fundersnot available
KeywordsDentistryMedicineLead acetateTooth eruptionSignificant differenceMaxillary central incisorIncisorOrthodonticsToxicityInternal medicineMolar

Abstract

fetched live from OpenAlex

OBJECTIVES This objective of this study was to evaluate the role of a heavy metal- lead acetate in the eruption rate of hypo functional incisors in albino Wistar rats. METHODOLOGY An experimental study was done in animal house of Post Graduate Medical Institute, Lahore since March 2019 to March 2020. 34 adult albino Wistar rats were randomly divided into two groups (n=17 for each group) i.e., control and lead acetate group. Right mandibular incisors were selected for this study. Selected incisors were marked 1mm above the level of gingival papillae. The incisors were cut above this mark to make it hypo-functional. The readings were measured by digital Vernier caliper. This was considered as day 0. Incisors length was measured at day 0, 3, 6, 12 and 15 and eruption was calculated. The data was analyzed using SPSS version 22. RESULTS Eruption rate was similar throughout the study except last follow up. At the end of this study eruption of incisors in albino Wistar rats in control was 03.30±0.72mm, in lead 02.43±1.19mm. At day 15, the difference between control and lead group was statistically significant (p-value 0.033). CONCLUSION These results reveal that besides other causes of delayed tooth eruption excessive lead intoxication are also acausative factor of delayed tooth eruption.

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)0.007
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 score0.234
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.046
GPT teacher head0.436
Teacher spread0.389 · 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
Published2022
Admission routes1
Has abstractyes

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