Bibliographic record
Abstract
UNLABELLED: The endonasal surgical approach of the lacrymal sac assisted by video-endoscopy is carried out today with high success rates. Despite the satisfactory results reached with the traditional external approach, it has the disadvantage of requiring a skin incision and a consequent local scar. With the development and enhancement of the endonasal techniques, the endoscopic approach is increasingly preferred by surgeons. OBJECTIVE: This paper reviews the lacrymal system anatomy, the preoperative assessment and the technical details of the endoscopic assisted approach which may provide better surgical outcomes for patients. We will also briefly discuss complications and causes for surgical failure. METHODOLOGY: This is a review of the experience of the authors in the past 10 years of employing the endoscopic technique for the lacrymal sac surgery. CONCLUSION: Outcomes regarding the endoscopic dacryocystorhinostomy are, at leas, equal to those from the traditional external approach. Notwithstanding, the joint work between the otorhinolaryngologist and the ophthalmologist is of great benefit to patients with epiphora.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".