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Record W7117147585 · doi:10.1093/jscr/rjaf1013

The ‘Sandwich dressing technique’ for management of the palate following soft tissue augmentation procedures: a case series

2025· article· en· W7117147585 on OpenAlexaff
Matthew K Morris, Aditya B Patel, Chris R Cameron, Anjali Y. Bhagirath

Bibliographic record

VenueJournal of Surgical Case Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicPeriodontal Regeneration and Treatments
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSoft tissueHemostasisSoft palateConnective tissuePatient satisfactionCosmetic Techniques

Abstract

fetched live from OpenAlex

Patient comfort after soft tissue augmentation procedures (STAP), most commonly free gingival grafts (FGGs) and connective tissue grafts (CTGs), is largely determined by morbidity at the palatal donor site. Pain, bleeding, and swelling at this site are the most frequently reported causes of dissatisfaction and may limit willingness to undergo future STAP. There is a need for simple, reproducible methods that improve donor-site comfort without adding operative complexity or cost. The 'sandwich dressing technique' is a straightforward, reproducible palatal donor-site dressing technique designed to enhance hemostasis and comfort following STAP. Across the series, the sandwich dressing technique was feasible to implement, required no additional specialized materials, and added minimal chairside time. Patients reported acceptable comfort and manageable postoperative symptoms, with no unexpected adverse events or dressing-related complications. Within this case series, the sandwich dressing technique is a practical, easily adopted approach for palatal donor-site management after STAP.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.224

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.016
GPT teacher head0.340
Teacher spread0.324 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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