A Comparison of Transcanalicular, Endonasal, and External Dacryocystorhinostomy in Functional Epiphora: A Minimum Two-Year Follow-Up Study
Bibliographic record
Abstract
Purpose: To compare the outcomes of transcanalicular diode laser-assisted dacryocystorhinostomy (TCL-DCR), nonendoscopic endonasal dacryocystorhinostomy (NEN-DCR), and external dacryocystorhinostomy (EXT-DCR) as first-line treatments for functional epiphora. Methods: This multicenter, retrospective, case-control study included 135 eyes of 135 patients with functional epiphora (86 females and 49 males). Functional epiphora was diagnosed based on a patent lacrimal system with a delay in the fluorescein dye disappearance test (FDDT) or dacryoscintigraphy (DSG) and no ocular surface or eyelid abnormalities. The patients were treated with TCL-DCR (2008-2011) or Ext-DCR (2005-2008, 2011-2017) at Beyoglu Eye Research Hospital (Istanbul, Turkey) and NEN-DCR at Carrot Eye Surgery Clinic affiliated with the McMaster University (Hamilton, ON, Canada) (2010-2016). Success was defined as the absence of epiphora and the normalization of an earlier delayed FDDT after surgery. Results: The TCL-DCR, NEN-DCR, and EXT-DCR groups consisted of 38, 47, and 50 eyes with 25.9, 44.2, and 45.9 months of follow-up. The success rate for TCL-DCR was 65.8%, 70.2% for NEN-DCR, and 84.0% for EXT-DCR. During the follow-up period, 13.2% of TCL-DCR cases and 6.4% of NEN-DCR cases developed an anatomic obstruction of the lacrimal system. Conclusion: The EXT-DCR group had a higher success rate in the management of functional epiphora than the NEN-DCR and TCL-DCR groups and was significantly safer in terms of an iatrogenic anatomic block of the lacrimal system.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".