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Detection, prevention, and rehabilitation of amblyopia

2000· review· en· W4255723588 on OpenAlexaff
Gaétan Laroche

Bibliographic record

VenueCurrent Opinion in Ophthalmology · 2000
Typereview
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSurpriseMedicineRehabilitationReliability (semiconductor)Relevance (law)Value (mathematics)OptometryCompliance (psychology)Physical therapyPsychologyComputer scienceSocial psychologyMachine learning

Abstract

fetched live from OpenAlex

We are seemingly entering into a photoscreening era in this new year 2000. An increasing number of publications in 1999 have dealt with this particular method of detecting vision problems in children. In the same periods, interesting and promising developments on the rehabilitation of amblyopia have been reported. Moreover, the problem of compliance with amblyopia therapy seems finally to have been solved with the help of microchips and heat sensors! On the other hand, reports on both pharmacologic and penalization rehabilitation methods continue to show good reliability and clinical relevance. Finally, as in a previous review in 1998, we feel compelled to bring up at least one controversial issue: it is no surprise to find at least two seemingly erudite recent publications reputing not only the validity of vision screening in children, but the value of the treatment of amblyopia itself. We report here a most important study that responds to the challenge.

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)0.003
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: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.118
GPT teacher head0.477
Teacher spread0.358 · 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

Citations3
Published2000
Admission routes1
Has abstractyes

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