Loss of Visual Acuity is the Main Reason Why Reading Addition Increases After the Age of Sixty
Bibliographic record
Abstract
PURPOSE: To determine why the reading addition increases after the age of 55 to 60 years when accommodation is zero. METHODS: Distance and near visual acuities, arm length, habitual near working distance, reading addition, and pupil diameter were measured in 44 subjects aged >60 years (mean, 72.9 +/- 5.7). Reading addition values were attained using three techniques: least-plus addition using both N-notation text and MN-READ text and the cross-cylinder technique. RESULTS: The mean dioptric working distance was 2.75 +/- 0.40 D. The reading addition found using N-notation text (+2.21 +/- 0.38 D) was significantly lower than that measured using MN-READ text (+2.48 +/- 0.49 D) or the cross-cylinder method (+2.53 +/- 0.44 D). The reading addition was positively correlated with the dioptric working distance (r = 0.47, p < 0.01), and decreasing habitual working distance was associated with poorer visual acuity (r = -0.42, p < 0.01). CONCLUSIONS: Our results suggest that decreases in near visual acuity after 60 years of age lead to a reduction in habitual working distance, which increases text angular subtense. In turn, the reduced working distance requires a greater reading addition. Increases in depth of field associated with both suprathreshold text (N-notation) and lower visual acuity lead to reading additions being less than the dioptric working distance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".