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EL MODELO DE EVALUACIÓN DE DIAGNÓSTICO DE ANDALUCÍA

2007· article· en· W2564506 on OpenAlexaboutno aff
Casto Sánchez Mellado

Bibliographic record

VenueAvances en supervisión educativa · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Teaching and Evaluation
Canadian institutionsnot available
FundersNational Institute on AgingU.S. Public Health ServiceU.S. Food and Drug Administration
KeywordsCurriculumCensusValue (mathematics)Character (mathematics)Political scienceSchool educationPrimary educationPedagogyPsychologySociologyMathematicsDemographyStatistics

Abstract

fetched live from OpenAlex

To assess whether long-term thiazide use is associated with a decreased risk of hip fracture, a nested case-control study was done in the Canadian province of Saskatchewan between 1984 and 1985 among residents who were 65 years of age or older and who were not receiving other drugs thought to affect bone mass. There were 905 hip fractures identified from hospital discharge records and 5137 population controls matched for age, sex, and calendar year. Drug use was ascertained from computerised pharmacy records. Risk of hip fracture decreased significantly with increasing duration of current thiazide use: relative risk (95% confidence interval) of 1.2 (0.9-1.5) for less than 2 years use, 0.8 (0.7-1.0) for use of 2-5 years, and 0.5 (0.3-0.7) for 6 or more years. In contrast, there was no such trend for use of other antihypertensive-diuretic drugs (relative risk 0.9 [0.6-1.3] for use of 6 or more years). This protective effect was not altered by age, sex, nursing home residence, previous hospital admission, or use of other antihypertensive-diuretic drugs or psychotropic drugs. Medical record review for a sample of 235 cases suggested this finding was not due to confounding by body mass, ambulatory status, functional status, or dementia. These results support the hypothesis that thiazides protect against osteoporosis in elderly people.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.032
GPT teacher head0.436
Teacher spread0.404 · 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.

Study designQualitative
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
Published2007
Admission routes1
Has abstractyes

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