Treatment outcomes in maxillofacial rehabilitation: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review is to assess the treatment outcomes following maxillofacial rehabilitation and to identify the tools used to evaluate those outcomes. INTRODUCTION: Maxillofacial defects caused due to tumor, trauma, or any pathology affects the patient physically, mentally, and psychologically. Various methodologies and strategies are used for jaw reconstruction and oral rehabilitation to help the patient regain the functions and quality of life that were lost due to the defect. The evaluation of these treatment outcomes is imperative to assess the success of rehabilitation. INCLUSION CRITERIA: The review will include patients with any maxillofacial defect caused by a developmental anomaly, trauma, or tumor. The patients must have undergone any type of reconstruction and/or rehabilitation and can be from any age group. All treatment outcomes of maxillofacial rehabilitation will be considered. Information from primary and secondary sources and from diverse geographical settings will be included. METHODS: This review will follow the JBI methodology for scoping reviews. Databases to be searched will include PubMed (Ovid), Scopus, PsycINFO (EBSCOhost), CINAHL (EBSCOhost), Web of Science, Cochrane CENTRAL, ProQuest Dissertations and Theses, and Google Scholar (first 10 pages of the search). Two independent reviewers will screen the titles and abstracts and extract data from selected studies. Data will be presented in tabular format, accompanied by a narrative summary. REVIEW REGISTRATION: Open Science Framework https://osf.io/dp8wc.
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 | 0.094 | 0.071 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.074 | 0.015 |
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".