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COMMON GROUND AND MODAL DISAGREEMENT

2007· article· en· W8712222 on OpenAlexaff
David Hunter

Bibliographic record

VenueLinguistic and Philosophical Investigations · 2007
Typearticle
Languageen
FieldArts and Humanities
TopicEpistemology, Ethics, and Metaphysics
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCommon groundModalCommon cause and special causeLinguisticsEpistemologyTRACE (psycholinguistics)Computer scienceMathematicsPhilosophyPsychologySocial psychologyStatistics

Abstract

fetched live from OpenAlex

To determine the possible association between anemia and clinical and echocardiographic cardiac disease, a cohort of 432 end-stage renal disease patients (261 on hemodialysis and 171 on peritoneal dialysis) who started dialysis therapy between 1982 and 1991 were followed prospectively for an average of 41 months. Baseline demographic, clinical, and echocardiographic assessments were performed, as well as monthly serial clinical and laboratory tests while the patients were on dialysis therapy. The mean (+/-SD) hemoglobin level during dialysis therapy was 8.8 +/- 1.5 g/dL. After adjusting for age, diabetes, and ischemic heart disease, as well as for blood pressure and serum albumin levels measured serially, each 1 g/dL decrease in mean hemoglobin was independently associated with the presence of left ventricular dilatation on repeat echocardiogram (odds ratio, 1.46; P = 0.018) and the development of de novo (relative risk [RR] = 1.28; P = 0.018) and recurrent (RR = 1.20; P = 0.046) cardiac failure. In addition, each 1 g/dL decrease in the mean hemoglobin level was independently associated with mortality while the patients were on dialysis therapy (RR = 1.14; P = 0.024). Anemia had no independent association with the development of ischemic heart disease while the patients were on dialysis therapy. Anemia, an easily reversible feature of end-stage renal disease, is an independent risk factor for clinical and echocardiographic cardiac disease, as well as mortality in end-stage renal disease patients.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.705

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 studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.105
GPT teacher head0.306
Teacher spread0.201 · 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 designTheoretical or conceptual
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

Citations1
Published2007
Admission routes1
Has abstractyes

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