Recommendations for Potassium-Titanyl-Phosphate Laser in the Treatment of Cholesteatoma
Bibliographic record
Abstract
Potassium-titanyl-phosphage (KTP) lasers possess many characteristics suitable for otologic surgery. The objective of this report is to provide recommendations on the use of KTP laser for cholesteatoma surgery based on a narrative review of currently available evidence. PubMed and the Cochrane Review of randomized control trials were searched for relevant publications on efficacy and adverse effects and relevant articles appraised by the authors using Oxford Centre for Evidence-based Medicine criteria for recommendations. The potential benefits of KTP laser in cholesteatoma surgery include reduced rates of residual cholesteatoma and improved hearing outcomes. Cholesteatoma may be more effectively removed using KTP laser than using mechanical dissection alone. Reduced rates of residual cholesteatoma have been reported with KTP laser with level 2 evidence. In addition, KTP laser may facilitate the removal of cholesteatoma without the risk of mechanical trauma. This potentially allows for improved postoperative hearing outcomes through a) minimizing cochlear trauma and reducing sensorineural hearing loss and b) reducing the need for disruption of an intact ossicular chain. Currently, level 4 evidence exists to support improved postoperative hearing outcomes with the use of KTP laser. KTP laser use appears to be safe in otologic surgery if appropriate guidelines are followed. Current evidence is sufficient to strongly recommend KTP laser use for the prevention of residual cholesteatoma (Grade B recommendation) and an option for KTP laser use for optimizing hearing outcomes (Grade C recommendation).
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".