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Record W3121716864 · doi:10.3389/fdmed.2020.632534

100 Years of Chalky Teeth Research: From Pioneering Histopathology to Social Good

2021· article· en· W3121716864 on OpenAlexaff
Michael J. Hubbard, Vidal Pérez, Bernhard Ganss

Bibliographic record

VenueFrontiers in Dental Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicBone and Dental Protein Studies
Canadian institutionsUniversity of Toronto
FundersUniversity of Melbourne
KeywordsTranslational researchEnamel hypoplasiaDental researchEnamel paintPsychologyMedicineDentistryPathology

Abstract

fetched live from OpenAlex

One hundred years ago, histopathology pioneer Bernhard Gottlieb described developmentally disrupted teeth as having “chalky enamel” and “chalky spots” that “crumble” easily. He also asked pivotal questions about the pathogenesis of “enamel hypoplasia” that remained enigmatic for almost a century. Today, breakthrough pathomechanistic investigations of chalky enamel are revealing surprising answers, and an allied translational initiative—The D3 Group for developmental dental defects (“D3s”) —is converting such scientific knowledge into social good surrounding prevention of tooth decay. Molar hypomineralisation (MH) affects 1-in-5 children worldwide and is well-evidenced, but poorly recognised, as a principal risk factor for childhood tooth decay. Given MH is causally linked to infantile illness, an exciting corollary is that medical prevention would lead to substantial reductions in decay. Here we reflect on the past century of chalky teeth research and retrace the path leading to recognition of MH as a global health concern. Five research eras, today's four major D3s, and diverse experimental attacks are outlined alongside translational wins that have benefitted global health. Addressing hopes for medical prevention of MH, this centennial year's pathomechanistic discovery is contextualised against past accomplishments and new opportunities. Finally, we note the translational value of accessible infographics for guiding future work, and forecast exciting prospects for the next century.

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)0.001
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 score0.387
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.071
GPT teacher head0.361
Teacher spread0.290 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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