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Record W2066818735 · doi:10.1371/journal.pone.0092038

Quality and Quantity of Information in Summary Basis of Decision Documents Issued by Health Canada

2014· article· en· W2066818735 on OpenAlexafffundabout
Roojin Habibi, Joel Lexchin

Bibliographic record

VenuePLoS ONE · 2014
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health ResearchAllerganAustralian Government
KeywordsClinical trialMedicineDrug trialDescriptive statisticsMEDLINEFamily medicineStatisticsInternal medicineMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: Health Canada's Summary Basis of Decision (SBD) documents outline the clinical trial information that was considered in approving a new drug. We examined the ability of SBDs to inform clinician decision-making. We asked if SBDs answered three questions that clinicians might have prior to prescribing a new drug: 1) Do the characteristics of patients enrolled in trials match those of patients in their practice? 2) What are the details concerning the drug's risks and benefits? 3) What are the basic characteristics of trials? METHODS: 14 items of clinical trial information were identified from all SBDs published on or before April 2012. Each item received a score of 2 (present), 1 (unclear) or 0 (absent). The unit of analysis was the individual SBD, and an overall SBD score was derived based on the sum of points for each item. Scores were expressed as a percentage of the maximum possible points, and then classified into five descriptive categories based on that score. Additionally, three overall 'component' scores were tallied for each SBD: "patient characteristics", "benefit/risk information" and "basic trial characteristics". RESULTS: 161 documents, spanning 456 trials, were analyzed. The majority (126/161) were rated as having information sometimes present (score of >33 to 66%). No SBDs had either no information on any item, or 100% of the information. Items in the patient characteristics component scored poorest (mean component score of 40.4%), while items corresponding to basic trial information were most frequently provided (mean component score of 71%). CONCLUSION: The significant omissions in the level of clinical trial information in SBDs provide little to aid clinicians in their decision-making. Clinicians' preferred source of information is scientific knowledge, but in Canada, access to such information is limited. Consequently, we believe that clinicians are being denied crucial tools for decision-making.

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)0.046
metaresearch head score (Gemma)0.333
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.973
Threshold uncertainty score0.994

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.333
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0150.022
Science and technology studies0.0020.001
Scholarly communication0.0060.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.350
GPT teacher head0.524
Teacher spread0.174 · 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
DomainReporting
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

Citations11
Published2014
Admission routes3
Has abstractyes

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