Surgiflo® hemostatic matrix versus <scp>NasoPore</scp>® nasal packing following <scp>postassium titanyl phosphate</scp> laser surgery for hereditary hemorrhagic telangiectasia: A randomized controlled trial
Bibliographic record
Abstract
Background: To help ensure adequate hemostasis immediately following potassium titanyl phosphate (KTP) laser treatment, many centres treating hereditary hemorrhagic telangiectasia (HHT) routinely use nasal packing post-operatively. The purpose of this study was to compare hemostatic thrombin matrix with standard packing for postoperative bleeding, patient pain, and comfort. Methods: A prospective, randomized, double-blinded, non-inferiority study was conducted with participants at an HHT centre of excellence (COE) and randomized to the treatment group with reconstituted thrombin gelatin matrix (Surgiflo®) or control group with a biodegradable synthetic polyurethane foam (NasoPore®). Adult subjects with confirmed HHT and moderate to severe epistaxis (a minimum calculated epistaxis severity score [ESS] of 4.0) warranting KTP laser treatment were recruited. Data was collected 2 weeks post operatively by a blinded reviewer completing a visual outcomes evaluation and each patient completing a subjective symptoms questionnaire. Non-parametric statistical analysis was employed. Results: = .005). While there were trends towards less obstruction and increased satisfaction in the treatment group as well as less crusting in the control group, these findings were not statistically significant. Allocation to the treatment group was associated with an approximately $75 higher cost. Conclusions: When compared to NasoPore® for hemostasis, Surgiflo® hemostatic matrix performed equivalently while causing less discomfort in HHT patients following nasal KTP treatment. Level of evidence: 1b.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".