Strong expression of HSP47 in metaplastic nasal mucosa may predict a poor outcome after primary endoscopic dacryocystorhinostomy: a prospective study
Bibliographic record
Abstract
PURPOSE: Dacryocystorhinostomy (DCR) is an effective and safe procedure for patients with post-saccal obstruction of the nasolacrimal pathway. The aim of DCR is to relieve symptoms by creating a bypass between the lacrimal sac and the nasal cavity. The most common reason for failure is stenosis caused by a fibrotic process at the rhinostomy site. In this prospective study we assessed the expression of heat shock protein 47 (HSP47), a regulator of fibrosis, in the biopsies of nasal mucosa isolated from patients undergoing primary endoscopic DCR (EN-DCR). METHODS: Thirty consecutive primary EN-DCR procedures in 30 patients were performed using the powered instrumentation technique. The nasal mucosa specimens over the rhinostomy site were collected for histological analysis at the beginning of the operation and the expression of HSP47 was evaluated by immunohistochemistry. The outcome of EN-DCR was estimated in follow-up visits at 1 week, 2 months and 6 months after surgery. RESULTS: At the 6-month follow-up, the overall success rate after primary EN-DCR was 83%. A metaplastic change and strong expression of HSP47 in nasal mucosa were associated with EN-DCR failure (p = 0.009). CONCLUSIONS: HSP47 may be regarded as a novel marker to predict impaired EN-DCR outcome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".