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Record W2130990167 · doi:10.17843/rpmesp.2014.311.18

Dificultades en la interpretación de los resultados de la investigación biomédica relacionada con el manejo de pacientes con enfermedades crónicas no transmisibles

2014· article· es· W2130990167 on OpenAlexaff
Gordon Guyatt, Germán Málaga

Bibliographic record

VenueRevista Peruana de Medicina Experimental y Salud Pública · 2014
Typearticle
Languagees
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHumanitiesMedicinePhilosophy

Abstract

fetched live from OpenAlex

The progressive increase in the prevalence of chronic non-communicable diseases (CNCD) has generated a need to change the paradigms in interpreting research about therapeutic and disease control strategies. One aspect to keep in mind is the incorporation of risk awareness that CNCD treatment implies, which creates uncertainty in the treatment result, compared to the curative paradigm that occurs in communicable diseases where a cure is expected. Another aspect is related to clinical trials result reports, where substitute results are used frequently. For example, the therapeutic goal of reducing glycosylated hemoglobin in a diabetic patient instead of showing the results based on treatment benefit (such as prevention of myocardial infarction). Problems arise when looking for a substitute that can replace the result that really matters. That is why we must be alert to the widespread use of results grouping (composite outcomes) which while they allow studies with fewer patients with shorter follow-up times and less expense, they can generate misleading results and show presumed untrue benefits due to improper selection of components of the "composite outcomes". In this article we draw attention to new challenges in the interpretation of scientific studies related to CNCDs.

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)0.028
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0280.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.078
GPT teacher head0.422
Teacher spread0.344 · 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 designNot applicable
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
Published2014
Admission routes1
Has abstractyes

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Same venueRevista Peruana de Medicina Experimental y Salud PúblicaSame topicHealth Systems, Economic Evaluations, Quality of LifeFrench-language works237,207