Sensitivity and specificity of pulp sensibility tests following traumatic dental injuries in permanent teeth: A one-year clinical study
Bibliographic record
Abstract
Objective: This study aimed to evaluate pulpal responses to sensibility tests after traumatic dental injuries and determine the sensitivity and specificity of these tests over time.Methods: Twenty-one patients with 51 traumatized teeth were included. After excluding 12 teeth during follow-ups, 39 teeth remained for final assessment. Pulp sensibility responses (electric pulp test (EPT), cold test, and heat test) were recorded at the initial visit and two weeks, one month, two months, and 12 months post-injury. The sensitivity and specificity of the tests were calculated using the response of traumatized teeth in the 12-month follow-up as the reference standard.Results: Lateral luxation was the most common injury. Among the 25 teeth with an initial negative response to sensibility tests, 4 concussions, 8 subluxations, 3 lateral luxation, 1 root fracture, and 1 uncomplicated crown fracture cases regained pulpal sensibility within one year. None of the immature teeth developed pulpal necrosis, whereas 7 out of 26 mature teeth did, primarily in lateral luxation cases. The specificity of the EPT increased from 0.47 on the first visit to 0.77 at two months and 0.83 at one year, with cold and heat tests showing similar trends. The sensitivity of cold and heat tests reached 1.0 at two months.Conclusions: Sensibility tests improved over time in traumatized teeth, with 17 out of 25 initially non-responsive teeth recovering within a year. No immature teeth developed necrosis. The specificity of all sensibility tests reached 0.83 in 12 months, and cold/heat test sensitivity reached 1.0 at two months.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".