Hierarchical Linear Modeling of Visual Acuity Change Over Time: Rate of Functional Recovery After Macular Hole Surgery
Bibliographic record
Abstract
PURPOSE: To examine acuity recovery rate after Macular Hole (MH) surgery, using Hierarchical Linear Modeling (HLM) with linear and curvilinear regression analysis. METHODS: Preoperative MH diameter (OCT) and acuity (ETDRS) were recorded in 20 eyes. Acuities were tested during follow-up (6 to 23 months), with three to eight measurements per eye. The resulting 95 acuities were analyzed using HLM. Variability at the level of the person was explained by change over time, using a natural logarithm conversion. Across patients, MH diameter was used to predict slopes and intercepts at the level of the individual. RESULTS: MH diameter was able to account for significant amounts of variability in preoperative acuity (intercept) and significantly influenced rate of functional recovery (slope). A nonlinear approach to the data accounted for the largest amount of variance. CONCLUSIONS: Participants with larger MHs recovered relatively more acuity sooner while eyes with smaller MHs had better absolute acuity outcome. HLM provides important insight into the recovery process after MH surgery and is more flexible with follow-up data. In the context of MH treatment, most recuperation occurred during the initial 6 months.
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 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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".