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Record W4394929976 · doi:10.1093/jbcr/irae036.316

775 A Unique Semirigid Silicone Neck Collar for Management of Hypertrophic Scars

2024· article· en· W4394929976 on OpenAlexaboutno aff
Michelle N Dwertman, Catherine Freeman

Bibliographic record

VenueJournal of Burn Care & Research · 2024
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCollarHypertrophic scarsSiliconeSurgeryCervical collarScars

Abstract

fetched live from OpenAlex

Abstract Introduction In the realm of burn injuries, the use of rigid hard neck collars, or soft collars, is indispensable for optimizing neck positioning and scar management. However, when injuries encompass the head and neck, a critical challenge arises only one rigid orthosis can typically be worn one at a time. We are introducing a novel solution: a semirigid neck collar crafted from flexible silicone. This collar combines the benefits of semirigid support with the freedom of natural neck and allows jaw movement and may be worn with a rigid facemask. Furthermore, it can be customized to effectively manage hypertrophic scarring, making it a promising advancement in burn injury care. Methods Our semirigid neck collar is crafted with High Consistency Rubber (HCR) silicone. The HCR can be fabricated as a solid sheet or perforated. This HCR applies a gentle, consistent minimum pressure of 20mmHg as displayed by the Kikuhime pressure monitoring device while remaining pliable enough to accommodate natural neck movements and TMJ mobility. The material is mixed, pigment tinted, and rolled to achieve the desired thickness. Following this, the HCR silicone is heated and molded based on a 3D scan of the patient's neck and fit to the patient. A successful trial was conducted in a patient with severe facial and neck burns, resulting in improved neck ROM and hypertrophic scar progression. The resulting scar displayed improvement on the Vancouver Scar Scale yielded as reported on the Likert scale and the patient reporting increased compliance of the semirigid collar. Results In the trial involving a patient with severe neck and facial burns: 1. Improved Neck ROM, Eating, and Mandibular Movement: While using the semirigid silicone neck collar, the patient experienced improvements in neck ROM, eating comfort, and mandibular movement. The average Likert scale rating for these improvements was five out of five. 2. Decreased Scarring upon Vancouver Scar Scale displaying the difference between the initial and final VSS scores, indicating how much the scar improved. 3. Patient Preference: A preference for the semirigid silicone neck collar, upon rating it a perfect five on the Likert scale. The patient was able to wear it in combination with a hard TFO facial orthotic. Conclusions The semirigid silicone neck collar represents a move forward in the field of neck orthosis design, particularly concerning scar management. Its innovative blend of design elements, including flexibility, adaptability, and customizable features, holds great promise for individuals dealing with burn-related neck scarring. Applicability of Research to Practice This semirigid appliance offers increased comfort compared to traditional rigid collars and an option in the management of neck positioning and scar hypertrophy. Its flexible nature, including color-tinting, and cost-effective modification options make it a valuable tool for improving scar management outcomes neck for patients with neck and combined facial/neck scarring.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.069
GPT teacher head0.438
Teacher spread0.368 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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