Recurrent Epiphora After Dacryocystorhinostomy Surgery: Structural Abnormalities Identified With Dacryocystography and Long Term Outcomes of Revision Surgery.
Bibliographic record
Abstract
Abstract Background: To investigate the aetiopathology of recurrent epiphora or stickiness after dacryocystorhinostomy (DCR) surgery, identifiable on dacryocystography (DCG), and to assess the success rates of secondary corrective surgeries.Methods: Consecutive post-DCR DCG images from patients with recurrent symptoms were reviewed between 2012-2015.Results: 159 eyes of 137 patients were evaluated. 58 DCGs showed normal postoperative findings, 4 an upper/lower canalicular block, 13 a common canalicular block, 31 a completely closed anastomosis, 50 a narrow anastomosis, and 3 an anastomosis draining into a nasal sinus.The most successful corrective procedures for each failure category were: Lester Jones Tube (LJT) for a normal post-operative DCG (17/18 success), Sisler trephination with tubes for upper/lower canalicular block (1/2 success), redo-DCR with tube for common canalicular blockage (5/6 success), redo-DCR +/- tube for completely closed anastomosis (12/16 success), LJT followed by redo-DCR +/- tube for narrow surgical anastomosis (1/1 and 17/27 success respectively), and redo-external-DCR with tube for anastomosis into a nasal sinus (1/1 success). Redo-DCR was ineffective in patients who had good post-DCR anatomical patency (22% success).Conclusion: This is the first study to report success rates of redo-DCR surgery according to anatomical findings confirmed by DCG. The outcome flow diagram help clinicians recommend procedures that are most likely to be successful for their patient’s specific anatomical abnormality. It also provides a visual tool for the shared decision-making process. Notably, symptomatic patients with a normal DCG post DCR are unlikely to benefit from redo-DCR, with a LJT being the recommended next step.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".