MétaCan
Menu
Back to cohort
Record W4321204465 · doi:10.12927/hcq.2023.27024

Overuse of Tests and Treatments: Has Canada Made Progress?

2023· article· en· W4321204465 on OpenAlexaffvenueabout
Alicia Costante, Xi-Kuan Chen, Alexey Dudevich, Antony Dennis Christy, Lyricy Francis, CH Chui

Bibliographic record

VenueHealthcare Quarterly · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsCanadian Institute for Health Information
Fundersnot available
KeywordsBest practiceMedicinePsychologyNursingFamily medicinePolitical science

Abstract

fetched live from OpenAlex

Overuse of healthcare services is a complex issue. Also known as low-value care, these are tests, treatments and procedures that are commonly ordered despite clear evidence that they do not help with patient care and may even cause harm. National clinician societies have developed over 450 Choosing Wisely Canada (CWC) recommendations to spur conversation about what is appropriate and necessary treatment. The latest report from the Canadian Institute for Health Information and CWC measured the trends and variation in the use over time of tests and treatments related to 12 CWC recommendations (CIHI 2022). Reductions in overuse were observed in eight of the 12 tests and treatments examined; findings for two of these measures - chronic benzodiazepine use and red blood cell transfusions - are highlighted. Despite some progress on reducing overuse, there remains considerable room for improvement in the appropriate and judicious use of tests and treatments in Canada.

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)0.009
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.889
Threshold uncertainty score0.805

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.036
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.009
Science and technology studies0.0050.003
Scholarly communication0.0060.003
Open science0.0030.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0110.001

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 head0.461
GPT teacher head0.520
Teacher spread0.059 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueHealthcare QuarterlySame topicHealthcare cost, quality, practicesFrench-language works237,207