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Record W2809328317 · doi:10.1002/jper.17-0062

Mean annual attachment, bone level, and tooth loss: A systematic review

2018· review· en· W2809328317 on OpenAlexaboutno aff
Ian Needleman, Raul I. García, Nikos Gkranias, Keith L. Kirkwood, Thomas Kocher, Anna Di Iorio, Federico Moreno, Aviva Petrie

Bibliographic record

VenueJournal of Periodontology · 2018
Typereview
Languageen
FieldDentistry
TopicOral microbiology and periodontitis research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePeriodontitisObservational studyMEDLINEClinical attachment lossTooth lossSystematic reviewCritical appraisalDentistryMeta-analysisAlternative medicinePathologyOral health

Abstract

fetched live from OpenAlex

Abstract Background Rate of progression of periodontitis has been used to inform the design of classifications of periodontal diseases. However, the evidence underpinning this topic is unclear and no systematic review has yet been conducted. Objectives The focused question for this systematic review was: in adults, what is the progression of periodontitis in terms of clinical attachment loss, radiographic bone loss, and tooth loss? Data sources Highly sensitive electronic search was conducted for published data in MEDLINE, EMBASE, LILACS, and unpublished grey literature in OpenGrey up to February 2016. Reference lists of retrieved studies for full‐text screening and reviews were hand‐searched for potentially eligible studies. Study eligibility criteria and participants Prospective, longitudinal observational studies with follow‐up of at least 12 months and presenting data on the primary outcome, change in clinical attachment level, in adults (age ≥18 years). Secondary outcomes, tooth loss and bone level change, were only assessed in studies reporting the primary outcome. Studies investigating specific disease populations or only on treated periodontitis patients were excluded. Study appraisal and synthesis methods Risk of bias and methodology were assessed using the Newcastle‐Ottawa Scale with two additional questions on security of outcome assessment. Studies were pooled by abstracting or estimating mean annual attachment or bone level change and annual tooth loss. Random effects meta‐analysis was conducted with investigation of effect of potential modifiers where possible. Results A total 11,482 records were screened for eligibility; 33 publications of 16 original studies reporting on more than 8,600 participants were finally included as eligible for the review. The studies represented populations from both developing and developed economies. Mean annual attachment loss was 0.1 mm per year (95% CI 0.068, 0.132; I2 = 99%) and mean annual tooth loss was 0.2 teeth per year (95% CI 0.10, 0.33; I2 = 94%). Observational analysis of highest and lowest mean attachment change quintiles suggested substantial differences between groups with minimal annual change in the lowest quintile and an average deterioration of 0.45 mm mean attachment loss per year in the highest group. This value increased to 0.6 mm per year with periodontitis alone. There was surprisingly little effect of age or gender on attachment level change. Geographic location, however, was associated with more than three times higher mean annual attachment loss in Sri Lanka and China (0.20 mm, 95% CI 0.15, 0.27; I2 = 83%) vs North America and Europe (0.056 mm, 95% CI 0.025, 0.087; I2 = 99%) P < 0.001. Limitations There were a limited number of studies (N = 16), high variability of design in key study components (sampling frames, included ages, data analyses), and high statistical heterogeneity that could not be explained. Conclusions Within the limitations of the research, the data show that mean annual attachment level change varies considerably both within and between populations. Overall, the evidence does not support or refute the differentiation between forms of periodontal diseases based upon progression of attachment level change.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.051
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0120.011
Bibliometrics0.0110.011
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.079
GPT teacher head0.402
Teacher spread0.324 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations105
Published2018
Admission routes1
Has abstractyes

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