The ‘20/20/20 Rule’ – When Good Intentions and Axiomatic Habit Displace Best Practices
Bibliographic record
Abstract
Optometrists often proffer the ‘20/20/20 Rule’ as advice for clients who experience nearpoint visual strain, or who are subjected to prolonged exposure to nearpoint devices. The ‘rule’ is offered in the patient’s best interests: To help alleviate asthenopia and visual stress from nearpoint strain, and to reduce the risk of onset or the progression of myopia and associated ocular disease. Best intentions aside, there is a paucity of clinical and scientific support for the rule. On the other hand, modern optical tools and methods, and vision rehabilitation practices are known to be helpful in addressing mild to severe binocular vision disorders, to promote comfort, and to slow the progression of myopia. While offering trite advice to address potentially serious concerns might appear to be helpful, its continued use could well be displacing other more appropriate management strategies. This paper addresses some concerns regarding the promulgation of this well-meaning, but misguided, advice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".