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Record W4288070019 · doi:10.1016/j.adro.2022.101039

Why Do Both Mean Dose and V≥x Often Predict Normal Tissue Outcomes?

2022· article· en· W4288070019 on OpenAlexaff
Lawrence B. Marks, Stefan A. Reinsberg, Ellen Yorke, Vitali Moiseenko

Bibliographic record

VenueAdvances in Radiation Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsUniversity of British Columbia
FundersNational Cancer InstituteAmerican Association of Physicists in MedicineMemorial Sloan-Kettering Cancer Center
KeywordsMedicineMetric (unit)Nuclear medicine

Abstract

fetched live from OpenAlex

In the Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC), High Dose per Fraction, Hypofractionated Treatment Effects in the Clinic (HyTEC), and Pediatric Normal Tissue Effects in the Clinic (PENTEC) reviews, the mean dose was identified as a reasonable predictor for risk of toxic effects in some normal organs,1-5 particularly in organs classically considered to have a parallel-like architecture, such as the lung, liver, and parotid. The utility of the mean dose as a predictive metric is puzzling, because an underlying biological basis is challenging to define.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.781
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.009
GPT teacher head0.336
Teacher spread0.327 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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