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Record W4239689275 · doi:10.51731/cjht..29

List of 2021 Health Technology Trends to Watch

2021· article· en· W4239689275 on OpenAlexaboutno aff
CADTH Horizon Scanning Service

Bibliographic record

VenueCanadian Journal of Health Technologies · 2021
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsnot available
Fundersnot available
KeywordsTelemedicineWearable computerHealth careHealth technologyEmerging technologiesPsychological interventionWearable technologyBiomedical technologyBusinessDisruptive technologyDigital healthMedicineComputer scienceInternet privacyEngineeringNursingPolitical science

Abstract

fetched live from OpenAlex

The CADTH Horizon Scanning Service identifies and monitors new and emerging health technologies likely to have a significant impact on health care in Canada. This Watch List focuses on those categories of medical devices, clinical interventions, or other health technologies that are, or may become, important or disruptive to Canadian health care or decision-making in the next few years. Among others, the trends and technologies featured in this Watch List include: developments of artificial intelligence within imaging technologies greater integration and connectedness of wearables to improving patient monitoring systems proliferation of transcatheter valve devices driving innovations for interventional cardiology increasing interest and development of non-pharmacological solutions for pain management rapid growth in technologies and digital services enabling virtual care

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)0.002
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.175
Threshold uncertainty score0.585

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0120.018
Science and technology studies0.0010.000
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.1750.090

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 head0.216
GPT teacher head0.502
Teacher spread0.286 · 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 designNot applicable
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

Citations2
Published2021
Admission routes1
Has abstractyes

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