Bibliographic record
Abstract
Aim: Many existing surgical techniques are used to treat fistula-in-ano (FIA) with known troubling complications such as frequent recurrence, pain, sepsis or incontinence.FiLaC (Fistula laser closure) is an uprising novel technique that uses laser diodes delivered via a flexible and radially emitting laser fibre to promote tract healing.Thermal energy is emitted into the fistula in a controlled manner, leading to collapse of the tract and accelerated healing.Its flexible tip allows energy delivery in convoluted tracts and completing the procedure in just a few minutes.Methods: This study reports our early experience with the use of FiLaC from March 2021 to July 2023 in 26 patients with primary, uncomplicated low type FIAs.Primary success rate, defined by clinical healing after single intervention, and surgical adverse events were reported.Results: Out of 26 patients, 19 (73.1%) were males and 12 (26.9%)were females with a mean age of 49.5 years.Seventeen patients (65.4%) had intersphincteric fistulas, 8 (30.8%) had transsphincteric fistulas, 1 (3.84%) had suprasphincteric fistula.All patients had history of seton insertion prior to receiving FiLaC.None of the patients had inflammatory bowel disease.The primary success rates was 53.8% (14 of 26).Twelve patients (46.2%) developed postoperative short term symptoms such as discharge and pain, while none of the patients complained of incontinence.Conclusion: FiLaC resulted in comparable primary success rates to traditional treatments and was not associated with major postoperative complications.It may be considered as a non-invasive and safe alternative to treat low type FIAs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.816 | 0.766 |
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".