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Mismatches in risk stratification between the UK and Canadian CF cohorts.

2023· other· en· W6923033311 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2023
Typeother
Languageen
FieldSocial Sciences
TopicPublic Administration and Political Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsRisk stratificationPopulationRisk assessmentRelative riskEpidemiologySubgroup analysis

Abstract

fetched live from OpenAlex

<p>Two prognostic models were constructed by AutoPrognosis separately on the UK and Canadian CF populations. Canadian CF patients with future outcomes of survival, death and LTx were annotated with circles, crosses, and triangles, respectively. Their locations were determined by the output of the two AutoPrognosis models. Very high-risk patients with FEV<sub>1</sub> below 30% predicted and supplement oxygenation were highlighted with cyan circles. The two AutoPrognosis models were in agreement of risk stratum for most of these very high-risk patients. Mismatches happened in two subgroups of patients with underestimated risk levels. The first subgroup consisted of moderate-risk patients that were identified as low-risk by the AutoPrognosis model developed on the UK population and was referred to as mismatch 1. The latter subgroup consisted of high-risk patients that fell into the moderate-risk stratum in the UK population. We referred to the corresponding area as mismatch 2.</p>

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.435
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.2090.001

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 head0.059
GPT teacher head0.327
Teacher spread0.268 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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