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Record W2793804316 · doi:10.1080/23809000.2018.1438845

Minimizing hematological toxicity in the management of anal cancer patients

2018· article· en· W2793804316 on OpenAlexaff
Kurian Joseph, Heather Warkentin, Karen Mulder, Corinne Doll

Bibliographic record

VenueExpert Review of Quality of Life in Cancer Care · 2018
Typearticle
Languageen
FieldMedicine
TopicColorectal and Anal Carcinomas
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsToxicityAnal cancerMedicineCancerOncologyInternal medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.110
GPT teacher head0.440
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

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