Efficacy of stents in endoscopic dacryocystorhinostomy surgery: meta analysis
Bibliographic record
Abstract
Objective: To determine if stents increase the success rate of endoscopic dacryocystorhinostomy (DCR-EN) Method: Systematic review of randomized clinical trials of DCR-EN, comparing surgeries performed with and without stents in children older than 10 years of age and adult patients, with primary lacrimo-nasal duct (LND) obstruction. The outcomes were LND patency and adverse events (complications). We searched the databases of Web of Science, Scopus, Embase, Cochrane, PubMed, Lilacs until May 2018. The RevMan 5.3 software provided by the Cochrane Collaboration was used for meta-analysis. Results: Ten studies involving 887 surgeries were included. Five studies were conducted in India, and each of the others were conducted in Canada, Turkey, Saudi Arabia, China and Finland. Lacrimo-nasal duct patency meta-analysis showed stents did not interfere in the chance of success in relation to non-use of the stents to obtain lacrimal viability (OR 1.62, 95% CI 1.00 to 2.64, I2 = 0%). It was not possible to perform a meta-analysis regarding the adverse effects and a descriptive analysis was made of the general complications and complications due to the stents. Conclusion: The use or no of stents in DCR-EN probably does not make any difference in the NLD patency. Further studies may better define whether there is a trend toward a better success rate with the use of stent. Key words: dacryocystorhinotomy, endoscopic, stent, success, meta-analysis
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.060 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".