Adapting new strategies in dental care to help geriatric and special needs patients during COVID-19 pandemic
Bibliographic record
Abstract
Purpose This paper aims to discuss and urge further deliberation on possible strategies to help geriatric and special needs patients to receive dental care during the pandemic. Design/methodology/approach This paper contains literature review of published research articles related to past epidemics, COVID-19 and older persons. Findings Accurate prediction of adverse outcomes, detection of unidentified problems, improved estimation of residual life expectancy and appropriate use of geriatric interventions is required to understand the necessity of the treatment and effect of possible COVID-19 contraction during the treatment. Research limitations/implications The authors reviewed the only published literature and collated the lessons learnt from past epidemics, as the natural history of the COVID-19 is not known. Practical implications Future dentists must be trained in crisis management to deal with pandemics more effectively. The dental fraternity should be equipped to provide some sort of “psychological counseling and reassurance” prior to dental care to vulnerable individuals with comorbidities and special needs. Originality/value There are very few published articles focused on unique dental care plans for geriatric and special needs patients.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".