Jaw-in-a-day (JIAD) for malignant indications: A systematic review
Bibliographic record
Abstract
INTRODUCTION: Jaw-in-a-day (JIAD) achieves resection, maxillofacial reconstruction and immediate dental rehabilitation. While increasingly used, evidence in oncologic populations remains limited. Our objective was to review JIAD in patients with head and neck cancer (HNC) and its outcomes. METHODS: Our systematic review was conducted following a predefined protocol (PROSPERO CRD420251043510). Our search strategy was executed on MEDLINE, Embase, CENTRAL, Web of Science, and CINAHL from January 2013 to October 2025. Studies with HNC patients of all ages who underwent JIAD were included. The Methodological Index for Non-Randomized Studies (MINORS) was used for risk of bias assessment. RESULTS: Nine studies were included with 57 patients. Common tumour characteristics included mandibular location (n = 31; 54.4 %) and squamous cell carcinoma pathology (n = 37; 64.9 %). A fibular free flap was used in all cases (n = 57; 100 %). Majority underwent adjuvant therapy (n = 36; 63.2 %). Prosthesis survival was reported in 6 studies (n = 25/32; 78.1 %) with follow-up periods ranging from 5 to 52 months. Prosthesis removal was commonly due to osteoradionecrosis (ORN) (n = 6; 10.5 %). All irradiated implants had successful early osseointegration (n = 99; 100 %). Delayed implant failure was reported in 4 patients (n = 4/36; 11.1 %) with 11 implants in radiotherapy field (n = 11/99; 11.1 %). CONCLUSION: JIAD for HNC patients can have high success in majority of select patients/defects. Although early osseointegration rates were high, oncologic patients receiving adjuvant radiation may remain at elevated risk for ORN-associated prosthesis failure. As such, the risks and benefits of JIAD should be carefully reviewed with the patient, and cancer staging should also be considered.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".