ACCURACY OF CLINICAL AND RADIOGRAPHIC MEASUREMENTS OF PERIODONTAL INFRABONY DEFECTS OF DIAGNOSTIC TEST ACCURACY (DTA) STUDIES: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to determine the accuracy of clinical and radiographic measurements of infrabony periodontal defects. METHODS: The MEDLINE-Pubmed and Cochrane-CENTRAL electronic databases were searched from initiation to May 2020. The inclusion criteria were clinical trials, human subjects with at least one infrabony defect, measurements of clinical attachment level (CAL), radiographic bone level (rBL), and intraoperative bone level (iBL) used as the reference standard. RESULTS: In total, 11 studies including 17 comparisons were included in this meta-analysis. All 17 comparisons showed that the values of the CAL and rBL measurements underestimated the iBL values. For CAL, the analysis showed a significant difference of means of -1.22 (P < .00001; 95%CI: [-1.49; -0.95]) and for rBL -1.10 (P < .00001; 95%CI: [-1.34; -0.85]). No significant DiffM were observed between the CAL and rBL measurements (DiffM -0.05; P = .76; 95%CI: [-0.39; 0.28]). CONCLUSION: The results of this systematic review and meta-analysis show that both clinical and radiographic measurements substantially underestimate the bone level when compared to intraoperative level measurements. However, there was no significant difference in the results between the clinical attachment level measurements and the radiographic observation.
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 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 | 0.039 | 0.109 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".