Screening for Dental Infections Achieves 6-Fold Reduction in Dental Emergencies During Induction Chemotherapy for Acute Myeloid Leukemia
Bibliographic record
Abstract
PURPOSE: Patients with newly diagnosed acute myeloid leukemia (AML) are at risk of infection, including odontogenic infections, during induction chemotherapy. It is unknown whether clinical dental screening to diagnose and treat odontogenic disease in these patients can reduce the incidence of dental emergencies. METHODS: Between November 1, 2014, and December 31, 2016, we screened 147 patients with newly diagnosed AML before their admission for induction chemotherapy (n1 = 147, “screened” group). The patients not screened acted as controls (n2 = 190, “unscreened” group), as did patients diagnosed with AML in the 26 months before the initiation of the screening program (n3 = 304, “prescreening” group). The number of patients in each group who presented for emergency dental assessment during admission for induction chemotherapy was determined by 2 independent reviewers. RESULTS: Among the 147 patients in the screened group, only 1 patient presented with an infectious odontogenic emergency (0.68% [95% CI, −0.64% to 1.98%]). In the unscreened group, 8 developed an infectious odontogenic emergency during induction chemotherapy (4.21% [95% CI, 1.37% to 7.15%]), a statistically significant difference ( P = .046, a = 0.05). A similar rate of infectious dental emergencies was observed in the prescreening group (4.28% [95% CI, 2.0% to 7.2%]). CONCLUSION: Clinical dental screening before induction chemotherapy in patients with AML resulted in a 6-fold reduction in infectious dental emergencies during the induction period.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".