Static computer‐aided implant surgery ( <scp>s‐CAIS</scp> ) analysing patient‐reported outcome measures (PROMs), economics and surgical complications: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To systematically evaluate the scientific literature for patient-reported outcome measures (PROMs) in static computer-aided implant surgery (s-CAIS). METHODS: A PICO strategy was executed using an electronic (MEDLINE, EMBASE, CENTRAL), plus manual search up to 15-06-2017 focusing on clinical studies investigating s-CAIS with regard to patients' pain & discomfort, economics and/or intra-operative complications. Search strategy was assembled from multiple conjunctions of MeSH Terms and unspecific free-text words. Assessment of risk of bias in selected studies was made at a "trial level" applying the Cochrane Collaboration Tool and the Newcastle-Ottawa Assessment Scale, respectively. RESULTS: The systematic search identified 112 titles. Seventy abstracts were screened, and 14 full texts were included for analysis. A total of 484 patients were treated with s-CAIS for placement of 2,510 implants. Due to the heterogeneity of the included studies, meta-analyses could not be performed. CONCLUSIONS: The number of identified studies investigating s-CAIS for PROMs was low. Scientifically proven recommendations for clinical routine cannot be given at this time; however, the number of clinical complications with s-CAIS seems to be negligible and comparable to conventional implant surgery. s-CAIS may offer a beneficial treatment option in edentulous cases if a flapless approach is applicable. Nevertheless, the economic effects in terms of time efficiency and treatment costs are unclear. Clinical investigations with well-designed RCTs investigating PROMs with standardized parameters are compellingly necessary for the field of s-CAIS.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".