Evidence map›Paper›PMID 41328918›Full record

SynthesisOral health & preventive dentistry2025

Relevance of Prognosis Tools in Periodontitis: A Systematic Review.

Amélie Sarbacher, Pierre-Yves Gegout, Olivier Huck

Abstract readSystematic Review
In one paragraph

Synthesis in Oral health & preventive dentistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Amélie Sarbacher
Pierre-Yves Gegout
Olivier Huck

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTooth-level prognostic systems can be used for treatment planning and help to identify teeth at risk of being lost over time. However, several sets of criteria were proposed to estimate tooth prognosis in the context of periodontitis patients.

objectivesTo identify the prognosis tools available and to evaluate their relevance in predicting periodontal-related tooth loss (TLP). METHODS AND MATERIALS: An electronic search was conducted for published data in MEDLINE, EMBASE, COCHRANE up to January 2025. Reference lists of retrieved studies for full-text screening and reviews were hand-searched for potentially eligible studies.

resultsIn total, 1,471 records were identified from databases, and an additional 7 unique records were identified through citation searching of selected studies from the database search. After screening, 33 studies were selected for full-text review, of which 22 were included in the final selection, with 6 classical models, 11 regression-based models, 2 AI-driven prognostic models and 3 external validations. Most prognostic models confirm high precision and accuracy, fluctuating in most cases around a value of area under the curve (AUC) = 0.8.

conclusionsPatient (smoking, diabetes) and tooth-related factors (furcation involvement, increased probing depth, mobility) influence the prognosis of tooth retention in the long term. The identification of prognostic factors is of crucial importance to better predict the long-term survival of teeth and to adapt the treatment plan. All prognostic systems at the dental level showed excellent predictive capacity for the risk of tooth loss linked to periodontitis. An AI-based machine-learning algorithm will be a helpful tool for determining tooth prognosis.

Indexed as

PeriodontitisTooth LossHumansPrognosisRisk Factorsperiodontitisprognosisregression treerisk factorstooth retention

Identifiers

PMID41328918
PMCPMC12673216

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Registered trials

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