The Better Operative Outcomes Software Tool (BOOST) Prospective Study: Improving the Quality of Cataract Surgery Outcomes in Low-Resource Settings
Why this work is in the frame
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Bibliographic record
Abstract
Purpose Post-operative vision impairment is common among patients who have undergone cataract surgery in low-resource settings, impacting quality of clinical outcomes and patient experience. This prospective, multisite, single-armed, pragmatic validation study aimed to assess whether receiving tailored recommendations via the free Better Operative Outcomes Software Tool (BOOST) app improved surgical outcomes, as quantified by post-operative unaided distance visual acuity (UVA) measured 1–3 days after surgery.Methods During the baseline data collection round, surgeons in low and middle-income countries recorded clinical characteristics of 60 consecutive cataract cases in BOOST. Additional data on the causes of poor outcomes from 20 consecutive cases with post-operative UVA of <6/60 (4–12 weeks post-surgery) were entered to automatically generate tailored recommendations for improvement, before 60 additional consecutive cases were recorded during the follow-up study round. Average UVA was compared between cases recorded in the baseline study round and those recorded during follow-up.Results Among 4,233 cataract surgeries performed by 41 surgeons in 18 countries, only 2,002 (47.3%) had post-operative UVA 6/12 or better. Among the 14 surgeons (34.1%) who completed both rounds of the study (1,680 cases total), there was no clinically significant improvement in post-operative average UVA (logMAR units ±SD) between baseline (0.50 ± 0.37) and follow-up (0.47 ± 0.36) rounds (mean improvement 0.03, p = 0.486).Conclusions Receiving BOOST-generated recommendations did not result in improved UVA beyond what could be expected from prospective monitoring of surgical outcomes alone. Additional research is required to assess whether targeted support to implement changes could potentiate the uptake of app-generated recommendations and improve outcomes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it