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Record W4390192883 · doi:10.1002/alz.071622

Resource utilization for pre‐screening study patients with cognitive disorders.

2023· article· en· W4390192883 on OpenAlexaff
P Gervais

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsQ & T Research
Fundersnot available
KeywordsRandomized controlled trialMedicineRandomizationCognitionLimited resourcesFamily medicinePsychiatryInternal medicineRisk analysis (engineering)

Abstract

fetched live from OpenAlex

Abstract Background Drug development is a complex, expensive, lengthy, and multifaceted process. Patient recruitment tends to be a considerable and underestimated variable when it comes to planning and implementing a study. More information about evaluating patient sources and resource allocation will allow sites and sponsors to use their resources more effectively and to complete clinical projects earlier. Method This paper reviews the sourcing of patients and resource utilisation in hours for 6 pivotal Cognitive Disorder studies by gathering all patients contacted in various phases of a project: pre‐screening, screening, and randomization. Result Sourcing of patients was tracked for pre‐screening, screening and randomisation and are compared in number of calls, resource utilisation (hours), and number of randomized patients A total of 1 726 patients were pre‐screened, which represents 575 hours of resources, the largest proportion of pre‐screened calls, 43%, was generated from internal database of patients resulting in 46% of randomized patients. The second largest proportion of pre‐screened patients originated from newspapers with 18% of contacts that required 13% of resources and resulted in 14% of randomized patients. Additional sources of pre‐screened calls were conferences (8%), canvassing (7%), friends (4%), medical references (3%), memory day (9%), radio (2%) and web site (2%). Conclusion We report a pre‐screened/screened/randomized ratio of 23:5:1 indicating that pre‐screening is important to secure patient enrolment though largely underestimated and rarely if ever accounted for in budget assessments. Given the current decline in the use of paper media, alternate solutions must be developed to replace this otherwise successful communication tool. More information is required to better characterize each recruitment method and to determine to which extent the randomization ratio is favorable with each initial source of 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 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)0.011
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.433
GPT teacher head0.534
Teacher spread0.101 · 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 designObservational
DomainMethods
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
Published2023
Admission routes1
Has abstractyes

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