Patient Satisfaction With Dental Implants in the Upper and Lower Arches Placed in a Tertiary Care Setting: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Dental implants offer functional and aesthetic rehabilitation for edentulous patients. Despite high survival rates, patient satisfaction hinges on more than osseointegration of titanium. This study explores subjective patient satisfaction with implants in maxillary and mandibular anterior and posterior regions in a tertiary care hospital, emphasizing aesthetic, emotional, and logistical dimensions. Methods: A qualitative design was used involving in-depth semi-structured interviews with 30 patients (17 females, 13 males; aged 35-80) who had received dental implants at least six months prior. Transcripts were thematically analysed using QDA (Qualitative Data Analysis) Miner Lite software (Provalis Research, Montreal, Canada). Coding was based on five predefined domains: overall treatment experience, treatment coordination, perceived aesthetic outcomes, functional challenges, and review and recommendations. Results: Of 30 patients, 26 reported an overall positive experience, one was neutral, and three were dissatisfied. Dissatisfaction arose from delays, travel difficulties, language barriers, and aesthetic mismatches. Emotional responses were positive: patients expressed joy in regaining chewing ability, confidence from restored appearance, and gratitude for empathetic care. Conversely, disappointed patients with aesthetic mismatches said they were not likely to recommend the centre or the treatment. CONCLUSION: Implant success must be evaluated holistically, incorporating patient emotions, expectations, and experience. Understanding the full patient journey, including non-clinical barriers, can help practitioners refine care delivery, communication, and prosthetic planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".