Role of chemotherapy and molecularly targeted agents in the treatment of adenoid cystic carcinoma of the lacrimal gland
Bibliographic record
Abstract
Adenoid cystic carcinoma (ACC) is the most common malignant epithelial cancer of the lacrimal gland. Despite a slow rate of growth, ACCs are ultimately associated with poor clinical outcome. Given the rarity of this disease, most recommendations regarding therapy are guided by expert opinion and retrospective data rather than level 1 evidence. Surgery and postoperative radiation therapy are commonly used as initial local treatment. In patients at high risk of recurrence, concomitant platinum-based chemotherapy may be added to postoperative radiotherapy in an attempt to enhance radio-sensitivity. While encouraging responses have been reported with intra-arterial neoadjuvant chemotherapy, this strategy is associated with substantial toxicity and should be considered investigational. For patients with metastatic disease not amenable to surgery or radiotherapy, chemotherapy may have a role based on its modest efficacy in non-lacrimal ACC. Similarly, molecular targeted agents may have a role, although the agents tested to date in non-lacrimal ACC have been disappointing. A better understanding of the biology of ACC will be crucial to the future success of developing targeted agents for this disease.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".