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Record W4366495712 · doi:10.1093/pch/pxac122

Ensuring access to innovative therapies for children, adolescents, and young adults across Canada: The single patient study experience

2023· review· en· W4366495712 on OpenAlexafffundabout
Gabriel Revon‐Rivière, Leah C. Young, Elizabeth A. Stephenson, Kathy Brodeur‐Robb, Sarah Cohen‐Gogo, Rebecca Deyell, Thierry Lacaze‐Masmonteil, Antonia Palmer, Rulan S. Parekh, James A. Whitlock, Daniel A. Morgenstern

Bibliographic record

VenuePaediatrics & Child Health · 2023
Typereview
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsUniversity of CalgaryWomen's College HospitalGovernment of AlbertaUniversity of AlbertaBC Children's HospitalC17 CouncilUniversity of TorontoSickKids FoundationHospital for Sick Children
FundersHealth CanadaHospital for Sick ChildrenBayer CanadaBC Children's Hospital
KeywordsMedicineExpanded accessLimitingClinical trialFamily medicine

Abstract

fetched live from OpenAlex

Innovative therapeutic approaches are needed to alleviate the burden of life-limiting, rare, and chronic conditions affecting children, adolescents, and young adults (CAYA). This includes a need for improved access to both clinical research and to non-approved or off-label therapies, together with, ultimately, more therapies achieving regulatory approval in Canada. The single patient study (SPS), also known as an open label individual patient (OLIP) study, was introduced by Health Canada to open access to non-marketed drugs where a clinical trial is not readily available, but the drug is considered too investigational to be managed on a standard Special Access Program. SPS is designed for patients who have a serious or life-threatening condition and have exhausted available treatment options. Our report summarizes this relatively new development in the Canadian regulatory environment and highlights the opportunities and challenges as identified by regulators, pharmaceutical representatives, academic researchers, and patient/parent advocates.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.955
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.123
GPT teacher head0.441
Teacher spread0.317 · 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 designOther design
Domainnot available
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

Citations4
Published2023
Admission routes3
Has abstractyes

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