Clinical utility of an optical coherence tomography middle ear scope: Interim results of the modification of antibiotic treatment intervention in children (OTO-MATIC) pragmatic cluster randomized controlled trial (RCT)
Bibliographic record
Abstract
OBJECTIVES: To evaluate the impact of a novel Optical Coherence Tomography (OCT) otoscope on the number of antibiotic prescriptions written for pediatric patients presenting to a primary care office with ear-related complaints, compared to the Standard of Care (SOC), a traditional otoscope. STUDY DESIGN: Planned interim analysis of the One Year OTO-MATIC Randomized Controlled Trial (RCT), multicenter, real-world effectiveness study. Pediatric patients presenting with ear-related complaints were seen by a provider previously randomized into the SOC or Intervention arm. The primary outcome was reduced antibiotic prescriptions (clinician rate and number of rounds per patient) for Intervention participants compared to the SOC participants. Secondary outcomes included changes in treatment recommendations at Baseline Visit (BV), including singular versus multimodal treatments, and referrals to an otolaryngologist, specifically. RESULTS: At the time of the interim database lock, there were 248 participants enrolled across four sites and 16 providers who had completed the BV. Our results demonstrate that the OCT intervention reduced the odds of antibiotic prescribing by 50 % compared to the SOC (OR = 0.50, 95 % CI: 0.45-0.56). Additionally, providers in the Intervention group were significantly more likely to initiate a single therapeutic modality versus multiple, often disparate modalities (91.6 % vs. 73.8 %, p < 0.001, respectively). CONCLUSIONS: Interim results suggest the OCT imaging technology (OtoSight, PhotoniCare) improves antibiotic stewardship with clinicians in the OCT arm having a reduced likelihood of prescribing antibiotics compared to the SOC arm. Overall, changes in provider prescribing patterns and therapeutic management of the patient are consistent with increased diagnostic certainty.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".