Correlation Between Pulp Sensibility Tests and Histologic Diagnosis
Bibliographic record
Abstract
Introduction: Accurate diagnosis of dental pulp conditions plays a key role in selection of an appropriate treatment including conservative vital pulp therapy (treatable pulp) or root canal treatment (untreatable pulp). The purpose of this study was to assess the accuracy of sensibility tests and the correlation of pulp response to sensibility tests with histologic pulp condition . Material and Methods: Assessment of clinical signs and symptoms and sensibility test include thermal and electrical pulp tests were all performed for 65 permanent teeth. The normal pulp and reversible pulpitis were considered as treatable conditions while the irreversible pulpitis and necrosis ones were considered as untreatable condition. The teeth were then extracted and sectioned for histological analysis of dental pulp. Comparisons between histological treatable and untreatable pulp condition were performed with chi square analysis for sensibility test responses. Result: A significant difference was detected in the normal and a sharp lingered response to heat and cold tests with a marginally significant difference for no responses to cold test between two histological treatable and untreatable groups. There was significant difference in the negative response to electric pulp test (EPT) between histological groups. The kappa agreement between clinical and histological diagnosis of pulp condition was about 0.843(p<0.001). The accuracy of cold and heat tests and EPT to detect treatable pulp or untreatable pulp states were 78% and 74% and 62% respectively. The sensibility tests diagnosed untreatable pulpitis with a higher probability (NPV=63%-67% -54%, PPV=83%-91% -95% for heat, cold and EPT respectively). Conclusion: Sensibility test results had a higher likelihood to diagnose pulpal disease or untreatable pulp conditions. The result demonstrated a good agreement between clinical and histological pulp diagnoses
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".