Investigation of implant internal screw loosening under hyperbaric conditions: An in vitro study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The internal screw has an essential role in carrying abutment and prosthesis loading. Some dental implant patients such as scuba divers, mineworkers, and hyperbaric oxygen patients are exposed to hyperbaric atmospheres. The aim of this study was to investigate internal screw loosening under hyperbaric conditions. MATERIALS AND METHODS: Forty-two dental implants were placed into plexiglass blocks. Abutments were fixed with internal screws by 25 Ncm torque force. Groups were set as control, 2.8-bar air pressure, and 4-bar air pressure groups. Hyperbaric groups were subdivided and exposed to either one or six cycles. Removal torque values were measured with a digital torque meter. Data were analyzed statistically using analysis of variance and Tamhane's T2 tests, considering p < 0.05 as the level of statistical significance. Internal screw threads were qualitatively observed under magnification. RESULTS: Four-bar groups showed the lowest removal torque values among all groups (p < 0.001). 2.8-Bar groups did not show significant differences compared with control groups. There was no statistically significant difference between the one and six pressure cycle subgroups in each group. All internal screw threads had various deformations. CONCLUSION: Four-bar air pressure had a significant effect on internal screw loosening. Internal screw tightening protocols should be reconsidered for patients exposed to advanced hyperbaric conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".