The Impact of COVID-19 on Dental Treatment in Kuwait – a Retrospective Analysis from the Nation’s Largest Hospital
Bibliographic record
Abstract
Background: The COVID-19 pandemic has changed the way dentistry has been practiced world over , this study sought to assess the impact of the COVID-19 pandemic on the patterns of attendance for dental treatment in a large hospital in Kuwait compare them to data from the year prior to the pandemic Methods: A total of 176,690 appointment records of 34,250 patients presenting to the AlJahra specialist hospital, Kuwait for dental treatment from April 2019 to March 2021 were analyzed. Types of procedures and the departments to which they presented were analyzed and the patterns of attendance before and during the pandemic were compared; Results: While there was a significant reduction in the number of orthodontic, endodontic and periodontal procedures there was no impact on oral surgery, restorative procedures or pediatric dentistry; Conclusions: There has been a return in the numbers of patients availing dental treatment, however, there has been a definite shift in the use of certain dental procedures .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".