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Record W7086605312

Estimation of Heights and Body Masses of Tuberculosis Patients in the Canadian Fluoroscopy Cohort Study for Use in Individual Dosimetry.

2021· article· en· W7086605312 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsFluoroscopyCohortTuberculosisCohort studyPopulationEstimationBody mass indexCohort effect
DOInot available

Abstract

fetched live from OpenAlex

AbstractThis paper documents the estimation of mean heights and body masses, by age and sex, used in development of organ-specific dose conversion coefficients for external radiation for a historical cohort of about 64,000 patients from the Canadian Fluoroscopy Cohort Study. Patients were exposed to repeated fluoroscopy and chest radiography examinations in the course of treatment for tuberculosis in residential medical facilities throughout Canada between 1930 and 1969. Using Canadian national survey data and extensive literature review, mean heights and masses were obtained for the White population of Canada during the time period of interest, and the differences in mean body mass between tuberculosis patients and the general population were estimated. Results in terms of mean height and body mass of Canadian tuberculosis patients, with uncertainties, are reported for selected age groups (children of ages 1, 5, 10, and 15 y and adults age 20+) and for both sexes. Use of estimated average heights and body masses by age and sex permits the adjustment of computerized phantoms for body mass for a given age, thereby increasing the relevance of the organ-specific dose conversion coefficients for the cohort and improving the accuracy of the resulting estimated organ doses.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.122
Threshold uncertainty score0.246

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.300
Teacher spread0.279 · 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 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
Published2021
Admission routes1
Has abstractyes

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