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Record W2034730998 · doi:10.1159/000067148

Raising Hemoglobin: An Opportunity for Increasing Survival?

2002· review· en· W2034730998 on OpenAlexaffabout
Gillian Thomas

Bibliographic record

VenueOncology · 2002
Typereview
Languageen
FieldMedicine
TopicEndometrial and Cervical Cancer Treatments
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsHemoglobinMedicineAnemiaHypoxia (environmental)Radiation therapyInternal medicineSurvival rateOncologyGastroenterologyPhysiologyOxygenChemistry

Abstract

fetched live from OpenAlex

Although the association between low hemoglobin levels and poorer outcomes in radiation oncology has long been recognized, anemia is often overlooked and untreated. However, a growing body of clinical evidence now indicates that low hemoglobin levels during radiation treatment are associated with decreased response and survival following radiotherapy. For example, a large Canadian retrospective study in patients receiving radical radiotherapy for cervical cancer showed that the 5-year survival rate was 19% higher in those whose hemoglobin during radiation treatment was =12 g/dl compared to those with levels <12 g/dl. The data suggest that clinical trials need to be performed to determine whether increasing hemoglobin levels leads to improved local control and survival. The mechanism by which low hemoglobin levels could cause poorer outcomes is not well understood and needs further elucidation. It is postulated that lower hemoglobin levels resulting in decreased oxygen carrying capacity may lead to increased tumor hypoxia, radiation resistance and increased tumor angiogenesis. The interrelationship of low hemoglobin levels, hypoxia, tumor angiogenesis and survival is explored in this article.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.343
GPT teacher head0.469
Teacher spread0.126 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations36
Published2002
Admission routes2
Has abstractyes

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