Minimizing hematological toxicity in the management of anal cancer patients
Bibliographic record
Abstract
Introduction: Hematological toxicity (HT) remains a major side effect of anal cancer (AC) treatment that can lead to unplanned treatment breaks and may affect clinical outcome.Areas covered: This review paper analyses the predictive factors related to HT and methods to minimize HT.Expert commentary: The destruction of red bone marrow (BM) stem cells are responsible for acute HT. BM damage is correlated with radiation dose and volume of BM irradiated. Functional imaging has been used to precisely quantify specific regions of active Pelvic BM . Studies using LKB modelling confirmed that PBM and LSBM act like parallel organs with a consistent volume effect in the development of HT. BM dose-volume constraints are recommended to minimise HT. BM-sparing IMRT plans incorporating active BM sites as avoidance structures resulted in significant reduction of dose to PBM without compromising target coverage and decreased the dose delivered to the functional BM volume. The increased incidence of HT is attributed more to MMC rather than IMRT. A single dose of MMC could be considered to minimize the incidence of HT. Clinical research should focus on newer more potent and potentially less toxic systemic agents to be used in combination with radiation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".