Bibliographic record
Abstract
Objectives: To assess the impact of COVID-19 outbreak on patient attendance at the dental clinic, University College Hospital, Ibadan and to make recommendations on how the oral healthcare services can adapt and evolve practices to appropriately care for increasing patients' load following the ease of lockdown.Materials and Methods: The attendance records of patients in the second quarter of 2019 and 2020 was retrieved from the medical records department of the Dental clinic of the University College Hospital, Ibadan and reviewed. Data collected included age, gender and the specialty clinic attended. Descriptive statistics were used to analyse the data. Frequencies and meanage were calculated and comparison of attendance was done using the student t test.Results: Three thousand, six hundred and seventy patients were seen in the second quarter of 2019 while 1276 were attended to during the same period in 2020. This showed a 66% decrease in clinic attendance in the period under review with reduction of 86.99% and 26.28% in April and June of these years respectively. The reduction in the attendance in the second quarter of 2019compared to the second quarter of 2020 was statistically significant (p=0.002).Conclusion: The COVID-19 epidemic is still a major public health concern that may still persist for some time therefore preventative measures are necessary to curtail the spread of this viral disease. Dental practitioners have an important role in this global fight for preventing the transmission of infectious diseases such as COVID-19 and must be trained ready for this role. It isrecommended that pragmatic approaches including standard infection prevention and control measures must be strictly adhered to in the oral health care settings to mitigate the spread of infection.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".