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Record W3201286685 · doi:10.15353/cjo.v83i3.1710

Approaches to Reduce or Eliminate the Risks of Sight Tests in Alberta

2021· article· en· W3201286685 on OpenAlexvenueaboutno aff
Alyssa Erin Anderson, Gordon Hensel

Bibliographic record

VenueCanadian journal of optometry/CJO. Canadian journal of optometry · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsnot available
Fundersnot available
KeywordsSightTest (biology)OptometryMedicine

Abstract

fetched live from OpenAlex

This research report is motivated by two independent case reports featuring individuals living in Alberta, Canada who experienced permanent vision loss as a result of inadequate standards of practice surrounding sight tests. Sight tests are usually performed by opticians and are conducted independently of a comprehensive eye exam. A description of the two case reports in addition to a discussion of the potential dangers of sight tests provide evidence of the public health risks associated with sight tests. To investigate potential approaches to reduce or eliminate the risks of sight tests in Alberta, we conducted a jurisdictional review examining the laws and standards of practice governing sight tests in Canada, the United States, New Zealand, and the United Kingdom. Based on the jurisdictional review, the outright prohibition of sight tests in Alberta may be the best approach to protect the public interest and reduce cases of avoidable vision loss. As seen in other Canadian provinces, alternative approaches to reduce the risk of sight tests may involve 1) developing and enforcing restrictions around the performance of sight tests or 2) developing clearly defined scenarios in which opticians can collaborate with authorized prescribers to deliver safe sight tests.

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)0.016
metaresearch head score (Gemma)0.039
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.096
Threshold uncertainty score0.360

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.039
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0060.003
Science and technology studies0.0070.004
Scholarly communication0.0050.001
Open science0.0050.004
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.238
GPT teacher head0.476
Teacher spread0.237 · 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
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

Citations1
Published2021
Admission routes2
Has abstractyes

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