A pattern of care analysis: Prosthetic rehabilitation of head and neck cancer patients after radiotherapy
Bibliographic record
Abstract
BACKGROUND: While some medical associations provide guidelines for the implant-prosthetic rehabilitation of head and neck cancer patients, the circulation and implementation in the everyday routine of practicing dentists remain unknown. PURPOSE: To analyze patterns of care for the prosthetic rehabilitation of head and neck cancer patients after radiotherapy in German speaking countries. MATERIALS AND METHODS: An online survey consisting of 34 questions separated into three sections, (a) general inquiries, (b) treatment concepts, and (c) patient cases, was forwarded to university hospital departments for Prosthetic Dentistry and Oral and Maxillofacial Surgery, and members of different medical associations. Statistical differences between groups were analyzed using chi-squared test (P < .05). RESULTS: From May to October 2019, 118 participants completed the survey. The majority practiced in university hospitals, had more than 5 years of work experience, and reported to be involved in <10 post radiation prosthetic rehabilitation cases per year. Rehabilitation protocols involving dental implants were implemented by oral/oral- and maxillofacial surgeons and prosthetic dentists, while general dentists favored implant-free solutions. Xerostomia was recognized as a common problem for a successful prosthetic rehabilitation. The subsequent treatment choice with either fixed dental prostheses or removable dentures was divided among participants. CONCLUSIONS: As treatment planning differed with regard to the participants' field of expertise and work environment, and most practitioners only handle a low number of cases, patients might benefit from centralization in larger institutes with a multidisciplinary structure. A high agreement between the practitioners' treatment concepts and the current state of research was observed. While the choice between a mucosa- or tooth-supported, and an implant-supported restoration depends on numerous individual factors, guidelines derived from longitudinal studies would enhance evidence-based treatment in this field.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".