Efficacy of a 3D-printed static progressive microstomia orthosis in increasing mouth opening and function following burn injury
Bibliographic record
Abstract
Microstomia, often observed following facial burn injuries, can hinder functional activities such as eating, speaking, dental care, and medical procedures such as intubation. To address these challenges, microstomia orthoses (MO) are used to increase mouth opening and function. Many versions of MOs have been described from hand-crafted to commercially available models. However, proposed options can be quite expensive based on fabrication time or materials, and not all models can be customized for patient's needs. 3D printing presents a promising alternative for fabricating MOs, addressing many of the limitations of currently used methods. The purpose of this study was to develop and evaluate the efficacy of a 3D-printed static-progressive MO in increasing mouth opening and improving functional outcomes. The orthosis was used to treat six adult burn survivors with pre-post evaluation using the Mouth Impairment and Disability Assessment (MIDA). The cost-effectiveness of this 3D MO was also investigated. A customizable 3D-printed static-progressive MO was co-designed by clinicians and two burn survivors, which cost approximately $2-3 USD of materials to produce. The prescribed wearing regime of this 3D-printed static-progressive MO resulted in significant improvement of vertical and horizontal mouth opening and MIDA scores with treatment and time. These results were achieved with patients wearing the MO 2-3x/day for 10-15 min, followed by completing their therapeutic activities and mouth exercises after wearing the MO. The global accessibility, low cost, and relative efficiency of this orthosis will be invaluable, particularly in resource poor regions. All 3D-printable files for this 3D-printed MO are publicly available (Appendix 1).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".