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Record W2067180843 · doi:10.1118/1.1711415

TCP isoeffect analysis using a heterogeneous distribution of radiosensitivity

2004· letter· en· W2067180843 on OpenAlexaff
Marco Carlone, David E. Wilkins, Balázs Nyíri, Peter Raaphorst

Bibliographic record

VenueMedical Physics · 2004
Typeletter
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsOttawa Regional Cancer FoundationCarleton University
Fundersnot available
KeywordsConfidence intervalPopulationStatisticsDistribution (mathematics)MathematicsNuclear medicineValue (mathematics)HomogeneousMedicineMathematical analysisCombinatorics

Abstract

fetched live from OpenAlex

A formula for the alpha/beta ratio is derived using the heterogeneous (population averaged) tumor control model. This formula is nearly identical to the formula obtained using the homogeneous (individual) tumor control model, but the new formula includes extra terms showing that the alpha/beta ratio, the ratio of the mean value of a divided by the mean value of beta that would be observed in a patient population, explicitly depends on the survival level and heterogeneity. The magnitude of this correction is estimated for prostate cancer, and this appears to raise the mean value of the ratio estimate by about 20%. The method also allows investigation of confidence limits for alpha/beta based on a population distribution of radiosensitivity. For a widely heterogeneous population, the upper 95% confidence interval for the alpha/beta ratio can be as high as 7.3 Gy, even though the population mean is between 2.3 and 2.6 Gy.

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)0.005
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.321
GPT teacher head0.505
Teacher spread0.184 · 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 designSimulation or modeling
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

Citations19
Published2004
Admission routes1
Has abstractyes

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