Surgical versus conservative management of tongue lacerations in the acute care setting: A systematic review of the literature
Bibliographic record
Abstract
Objective: The objective of this study was to determine whether suturing or conservative management of tongue lacerations results in differences in wound healing and functional outcome. The secondary aim was to identify whether antibiotics are required in the treatment of tongue lacerations. Methods: Studies published between December 1954 and August 2020 were extracted from MEDLINE via PubMed, Embase via OVID, CINAHL via EBSCO, Web of Science, and the Cochrane Library and evaluated for inclusion based on predetermined inclusion and exclusion criteria by two independent reviewers in accordance with PRISMA guidelines. Results: The search yielded a total of 16,111 articles, 124 of which were evaluated by full-text review, resulting in 11 articles included in this systematic review representing 142 unique cases of tongue lacerations. At least 26 lacerations (18.3%) included penetration of the muscle layer of the tongue, and 24 (16.9%) were classified as full-thickness lacerations. Thirty-five of the 142 tongue lacerations (24.6%) were sutured. The remaining lacerations underwent some form of conservative management. The majority of studies reported excellent healing of tongue lacerations regardless of the management method, with minimal scarring and excellent return to normal functional status. No cases of infection were reported. Conclusions: Current literature is inconsistent with regards to indications and guidelines for primary repair of tongue lacerations. The majority of tongue lacerations reported in the literature heal with excellent outcomes regardless of management method. Physician judgement along with patient and parental preference based on potential risks of the procedure should be used when deciding whether a tongue laceration requires primary repair. Tongue lacerations in otherwise healthy individuals are at very low risk of infection.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".