Bibliographic record
Abstract
The purpose of this study was to examine the effects of conscious sedation on pain and anxiety of patients in implant surgery. A total of 95 patients who underwent implant surgery were included in the study. In this study, the patient``s anxiety and pain to evaluate the pre-operative Visual Analogue Scale (VAS), during-operative Pain Question (PQ), post-operative (Short-form McGill Pain Questionnaire [SF-MPQ], VAS) was used for tools suchas questionnaires. The data were analyzed using the chi-squire, independent-samples t-test, multiple linear regression analysis. As a result, the pain reduction was significantly different between the sedative dental treatment and non-sedative dental treatment (p＜0.05). The finding of the study multiple linear regression analysis showed that operation time, implant surgery experience, gender, age, operation form and Pain CatastrophizingScale (PCS) with factors that affect the pain and anxiety (p＜0.05). According to the results of the study, considered to be necessary to develop intervention strategies effective using the PCS when managing pain and anxiety of behavior management of this implant patient. Thus, it is advised to provide necessary practical guidelines and dental utilization behaviors on patients with conscious sedation.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".