What are the important outcomes in traumatic dental injuries? An international approach to the development of a core outcome set
Bibliographic record
Abstract
BACKGROUND/AIMS: There are numerous treatment options following traumatic dental injury (TDI). Systematic reviews of different treatments are challenging owing to the diversity of outcomes reported between clinical studies. This issue could be addressed through the development and implementation of a agreed and standardized collection of outcomes known as a core outcome set (COS). The aim of this study was to develop a COS for TDI in children and adults. The secondary aim was to establish what, how, when and by whom these outcomes should be measured. MATERIALS AND METHOD: The project was registered with Core Outcomes Measures in Effectiveness Trials (COMET). A web-based survey was developed to capture the opinions of dentists globally as to which outcomes should be recorded. A list of outcomes was entered into a Delphi Survey and scored by an Expert Working Group (EWG). The scoring was repeated, followed by conference calls to discuss, refine and finalize the COS. The EWG split into small groups of subject-specific experts to determine how, when and by whom each outcome would be measured. RESULTS: The questionnaire was completed by 1476 dentists. The EWG identified 13 core outcomes to be recorded for all TDI's. An additional 10 injury-specific outcomes were identified. A table has been produced for each outcome detailing what, when, and how each outcome should be recorded. CONCLUSIONS: A robust consensus process was used to develop an international COS for TDI in children and adults. This includes both generic and injury-specific outcomes across all identified domains.
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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 | 0.551 | 0.525 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.022 | 0.016 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".