Survey on Usage of Dye Pharmaceutical Preparations in Sentinel Lymph Node Biopsy
Bibliographic record
Abstract
Sentinel lymph node biopsy (SLNB) has recently been accepted as a standard diagnostic procedure in the treatment of early breast cancer.However, since there are no dyes for this diagnostic technique on the market in Japan, we conducted a survey of the situation of dye use and preparations for SLNB in regional cancer treatment centers.We sent a questionnaire to 286 institutions for which the response rate was 75.2％.The results showed that 80.9％ of the institutions had already introduced SLNB and the dye-guided method was used by more than 90％ of them.The most common indication for SLNB was breast cancer.Patent blue prepared by hospitals and indigo carmine and indocyanine green (both off-label use) were commonly used dye preparations for SLNB.Although patent blue is the most satisfactory dye and has the highest identification rate, problems were reported with hospital preparations.There were also differences between institutions regarding storage conditions, validity dates, preparation standards and annual use of this dye.The survey also revealed that 74.4％ of institutions wanted dyes for SLNB to be marketed and there were numerous opinions on which types of patent blue preparation should be available.In conclusion, the present study indicated that there was a need for SNLB dye preparations in clinical practice and that it would be beneficial to make such preparations available on the market.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".