Dental patient‐reported outcomes following traumatic dental injuries and treatment: A narrative review
Bibliographic record
Abstract
Dental patient-reported outcomes (dPROs) are self-reported descriptions of a patient's oral health status that are not modified or interpreted by a healthcare professional. Dental patient-reported outcome measures (dPROMs) are objective or subjective measurements used to assess dPROs. In oral healthcare settings, the emphasis on assessing treatment outcomes from the patient's perspective has increased and this is particularly important after traumatic dental injuries (TDIs), as this group of injuries represent the fifth most prevalent disease or condition worldwide. The purpose of this review is to summarize the current use of dPROs and dPROMs in the field of dental traumatology. Oral Health-Related Quality of Life, pain, swelling, aesthetics, function, adverse effects, patient satisfaction, number of clinical visits and trauma-related dental anxiety are the key dPROs following TDIs. Clinicians and researchers should consider the well-being of patients as their top priority and conduct routine evaluations of dPROs using measures that are appropriate, accurate and reflect what is important to the patient. After a TDI, dPROs can assist clinicians and patients to choose the best management option(s) for each individual patient and potentially improve the methodology, design and relevance of clinical studies.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".