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Record W2333198492 · doi:10.1017/s0317167100005655

Improving Drug Trials for Mild to Moderate Alzheimer's Disease

2007· review· en· W2333198492 on OpenAlexaffvenue
David B. Hogan

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2007
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsHealth Sciences CentreUniversity of Calgary
Fundersnot available
KeywordsClinical trialDiseaseMedicineDrug trialAlternative medicinePlaceboMEDLINEPsychologyPsychiatryIntensive care medicinePathologyPolitical science

Abstract

fetched live from OpenAlex

Over the last two decades, numerous studies have been conducted on subjects with mild to moderate Alzheimer's disease. The objective of this paper was to review concerns raised in the literature about the design and methodology of these clinical trials and to make recommendations to deal with the limitations identified. Concerns raised in the literature include the following: undue focus on statistical rather than clinical significance; the need for further pharmacoeconomic evaluations; the nonrepresentativeness of the study populations; perceived inadequacies in the direct-comparison studies conducted to date; the limitations of open-label extension studies; the inability of standard psychometric tools to document all the relevant treatment effects; the ethics of placebo-controlled trials; and, problems caused by the actions of the regulatory authorities. Recommendations are made to deal with the issues raised.

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)0.018
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.646
GPT teacher head0.487
Teacher spread0.159 · 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.

Study designNot applicable
DomainMethods
GenreReview

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

Citations6
Published2007
Admission routes2
Has abstractyes

Explore more

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