Congenital nasolacrimal duct obstruction outcomes
Bibliographic record
Abstract
Objective: To characterize children with congenital nasolacrimal duct obstruction (NLDO) seen by a pediatric ophthalmologist in terms of initiation of tear duct massage and need for lacrimal duct probing. Design: Retrospective chart review. Participants: All children with a diagnosis of congenital NLDO managed by one pediatric ophthalmologist (SH) at a tertiary pediatric center (McMaster Children's Hospital, Hamilton, Ontario, Canada). Methods: Charts were reviewed to determine the percentage of patients requiring nasolacrimal duct probing. Secondary outcomes were percentage of patients who had tear duct massage, accurate or inaccurate, initiated by the referring provider. Results: Overall, 113 patients with a mean age of 1.8 ± 1.5 years at presentation were included, and 55 (48.7%) were male. Most patients (83.2%) were referred by a primary care provider (family physician or pediatrician) and the remainder by optometrists (13.3%) or ophthalmologists (3.5%). Accurate tear duct massage was initiated by referring providers in 23% of cases. Ultimately, 44.2% of cases required probing, and 4.4% were still pending follow-up at time of data collection. Of children who learned accurate massage technique, 56% avoided surgical intervention, even when proper massage is introduced after 12 months of age. Conclusion: Most patients referred to pediatric ophthalmology for congenital NLDO did not have an appropriate trial of tear duct massage before referral, and less than half of patients required probing after appropriate massage was initiated. Educating primary care providers and optometrists on proper initiation of tear duct massage may reduce the volume of congenital NLDO referrals to pediatric ophthalmology, a subspecialty with limited physicians and long wait times.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".