Methods for Clinical Assessment in Periodontal Diagnostics: A Systematic Review
Bibliographic record
Abstract
AIM: This systematic review aimed to answer the following PECOS questions: In human subjects with untreated periodontitis (Q1) or enrolled in supportive periodontal care (SPC) (Q2) (P), are there clinical assessment methods (E) other than the contemporary manual probe (C) that increase diagnostic accuracy or reliability when examining/screening for periodontitis (Q1) or when monitoring disease stability or progression (Q2) (O) as demonstrated in clinical studies (S)? MATERIAL AND METHODS: A single search strategy was devised to identify relevant studies addressing Q1 and Q2 from four electronic databases. The main clinical parameters considered were probing depth (PD) and clinical attachment level (CAL). Risk of bias (RoB) was assessed using a modified Newcastle-Ottawa scale. RESULTS: Of the 5417 identified titles, 26 studies were finally included. The evidence revealed that manual probes generally yielded higher PD values, while pressure-sensitive/electronic probes demonstrated a trend for higher inter- and intra-examiner reproducibility. No clear trend for the superiority of one probe over the other could be identified for Q1 or Q2. CONCLUSIONS: The outcomes of the present systematic review indicated no clear benefit from the use of pressure-sensitive/electronic probes over contemporary manual probes. Manual probes remain the clinical standard for the diagnosis and monitoring of periodontitis patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".