Effective treatment depth of photodynamic therapy after partial debulking of nodular basal cell carcinoma
Bibliographic record
Abstract
Significance Tumor debulking before topical photodynamic therapy PDT increases the efficiency, however, this raises question of how much of the residual lesion is actually treated. Approach BCC thickness was measured before and after debulking by ultrasound and related to PDT effectiveness. The debulked material was histologically evaluated, and 30 days after treatment, a 2 mm punch biopsy were performed. Results The thickness measured by ultrasound before and after debulking ranged from 0.9 to 2.3 mm(mean 1.8±0.4mm) and from 0.5 to 1.9 mm(mean 1.3±0.3mm), respectively. This represents a 30%reduction in lesion thickness(0.6±0.3mm average debulking depth)(p<0.001). Clearance rate was 86%, however, lesions less than 1.4mm thick after debulking had 100% clearance. Conclusions Obtaining the precise thickness of BCC using ultrasound imaging could allow successful PDT treatment of thicker BCC lesions as long as the post-debulking thickness is <2 mm, with complete clearance being achievable at <1.4 mm, suggesting that the use of ultrasound imaging is a valuable adjunct for the precise use of PDT in nodular BCC.
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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 | 0.000 | 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) | 0.001 | 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".