Clinical dilemma of selective caries removalin primary teeth: a scoping review
Bibliographic record
Abstract
Despite growing evidence supporting selective caries removal in managing deep caries, many practitioners still prefer to manage caries in a more invasive way.Although proven to be beneficial in preserving pulp vitality, there is a concern that leaving soft dentin behind will compromise the adhesion of dentin to restoration.This paper aimed to review the evidence supporting selective caries removal of deep dentin caries on primary teeth, and the arguments against the technique using a scoping review method.Various outcome parameters were applied to define and evaluate treatment success, including pulp and dentine responses, dentin micro-hardness, bacterial load of dentin under restoration, and restoration survival.A structured literature search of PubMed and Scopus databases was performed for relevant articles, using specific key words and based on a PICO-structured question: Does selective caries removal in carious deep dentin primary teeth show better results in terms of pulp exposure risk, restoration integrity, micro-hardness, and bacterial load of sub-restorative dentin compared with complete caries removal?Selective caries removal is a minimally invasive approach that evidently protects teeth from the risk of pulp exposure.Several aspects must be considered before making the clinical decision to selectively remove carious tissue in managing deep caries.All dental practitioners are expected to possess a thorough knowledge about pros and cons of selective caries removal to deliver evidence-based treatments to their 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 | 0.064 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".