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Interstitial Fluid Pressure in Cervical Cancer

2001· article· en· W2329158748 on OpenAlexaff
Michael Milosevic, Anthony Fyles, Rićhard P. Hill

Bibliographic record

VenueAmerican Journal of Clinical Oncology · 2001
Typearticle
Languageen
FieldMedicine
TopicEndometrial and Cervical Cancer Treatments
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineAngiogenesisOxygenationPerfusionRadiation therapyRadioresistanceHypoxia (environmental)Tumor hypoxiaCervical cancerCancerInternal medicinePathologyCancer researchOxygen

Abstract

fetched live from OpenAlex

Interstitial fluid pressure (IFP) is elevated in most malignant tumors, mainly as a result of the abnormal tumor vasculature that develops from unregulated angiogenesis. Theoretical models predict that IFP should correlate with capillary flow resistance in tumors, and therefore also with perfusion and oxygenation. However, a prospective clinical study in patients with cervical cancer at Princess Margaret Hospital failed to demonstrate a relationship between IFP and oxygenation. Despite this, high IFP was strongly associated with inferior survival after radiotherapy independent of clinical prognostic factors and tumor oxygen status. This suggests that IFP and direct needle oxygen measurements may provide information about different aspects of tumor oxygenation, such as chronic versus intermittent hypoxia. Alternatively, IFP may reflect an aspect of tumor biology that is largely unrelated to perfusion and oxygenation. One possibility is that tumors with high pretreatment angiogenesis levels, as indicated by high IFP, may be more radioresistant because the vascular endothelium is more likely to survive during and after treatment. The mechanistic link between elevated IFP and the abnormal tumor vasculature and the strong prognostic effect of IFP in our cervix study together suggest that drugs targeted at angiogenesis, when combined with radiotherapy, may lead to improved tumor control and patient survival.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.595
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.084
GPT teacher head0.475
Teacher spread0.391 · 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.

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

Citations26
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Clinical OncologySame topicEndometrial and Cervical Cancer TreatmentsFrench-language works237,207