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Record W3121581001 · doi:10.1002/cre2.401

Regular maintenance appointments after non‐surgical scaling and root planing support periodontal health in patients with or without dry mouth: A retrospective study

2021· article· en· W3121581001 on OpenAlexaff
Taylor V. Sparrow, Péter Fritz, Philip Sullivan, Wendy E. Ward

Bibliographic record

VenueClinical and Experimental Dental Research · 2021
Typearticle
Languageen
FieldDentistry
TopicOral microbiology and periodontitis research
Canadian institutionsBrock University
Fundersnot available
KeywordsMedicineDentistryDry mouthScaling and root planingPeriodontal diseaseOral healthRetrospective cohort studySalivaPeriodontitisSurgeryInternal medicineChronic periodontitis

Abstract

fetched live from OpenAlex

OBJECTIVE: Non-surgical scaling and root planing (SRP), as an initial form of periodontal treatment, followed by ongoing periodontal maintenance appointments is necessary to manage periodontal disease and prevent tooth loss. Saliva also has an essential role in oral health though the relationship between low salivary flow and periodontal outcomes has not been extensively investigated. This study determined if patients with dry mouth have similar clinical outcomes as patients without dry mouth when receiving regular periodontal maintenance after SRP. MATERIALS AND METHODS: This is a retrospective study that investigated clinical periodontal outcomes in patients with (n = 34) or without (n = 85) dry mouth who had undergone SRP 1 to 5 years prior and had routine periodontal maintenance. The presence of dry mouth was established based on a patient's unstimulated salivary flow rate. RESULTS: Probing depth for both patients with or without dry mouth was similar between groups and maintained 1 to 5 years following initial SRP. Improved probing depth achieved post-SRP was sustained regardless of dry mouth status. CONCLUSION: Patients with or without dry mouth did not exhibit different probing depths.

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 score0.009
Threshold uncertainty score0.902

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.049
GPT teacher head0.429
Teacher spread0.379 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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