Cost-Effectiveness of the Biozorb Device for Radiation Planning in Oncoplastic Surgery
Bibliographic record
Abstract
Purpose: With the extent of breast tissue manipulation using oncoplastic surgical techniques, there lies a challenge in marking the tumor bed for adjuvant radiation therapy planning. Two competing techniques in doing so exist and involve the traditional placement of surgical clips in the surgical tumor bed or the newer technique of placing a Biozorb marker in the tumor bed. Our goal was to perform a cost-utility assessment to see which tumor bed marking approach is more cost-effective. Based on device list prices and clinical outcomes from a comprehensive literature review, we assessed if an approach either dominated or had an incremental cost-utility ratio of less than $50,000/QALY since either would signify cost-effectiveness. Results: From a cost comparison, the Biozorb marker ($1250) was far costlier than the clip applier device ($50). Our PRISMA search (Figure 1) reviewed 133 articles for clip placement and 42 articles for Biozorb placement in oncoplastic surgery with 2 clip placement articles and 3 Biozorb articles meeting criteria. The available data for either marking technique suggests reasonable tumor bed identification for adjuvant radiation treatment without clear clinical advantages supporting one technique over the other. Overall clinical equivalence in the setting of a clear cost advantage suggests dominant cost-effectiveness in favor of clips. Conclusion: Using surgical clips to identify the tumor bed in oncoplastic surgery is dominant and more cost-effective over the Biozorb technique as clips are relatively inexpensive while both techniques reasonably identify the tumor bed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".