Evaluation of early wound healing scales/indexes in oral surgery: A literature review
Bibliographic record
Abstract
BACKGROUND: The quality of the healing response after oral and maxillofacial surgery (OMS) is influenced by the nature of the tissue disruption and the circumstances surrounding wound closure. The use of wound healing scales may help the surgeon anticipate and, when possible, intervene so that wound repair can progress favorably. MATERIALS AND METHODS: Studies reported in the OMS literature of the last 20 years that applied scales/indexes to monitor the wound healing process were reviewed. RESULTS: We identified eight scales/indexes that were developed for use in OMS, including three that are modifications of a previously reported scale. Most were applied in split-mouth trials of wound healing modifiers. CONCLUSION: Wound healing scales are infrequently used in OMS. Those that are available do not allow for an association of the outcome parameters, modifiers used, or effectiveness of the modifiers with the different phases of the wound healing process (inflammatory, proliferative, and remodeling). Moreover, there is no consensus regarding the time frames that should be evaluated or the preferred scale. On the basis of these findings, we suggest a novel scale that distinguishes among the wound healing phases and yields three subscale scores and a total score.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".