Subjective and Objective Variation of the Tear Film Pre‐ and Post‐Sleep
Bibliographic record
Abstract
PURPOSE: To date, few studies have correlated the overnight effects of the preocular tear film (POTF) with subjective symptoms. This study investigates the POTF volume and stability, bulbar hyperemia (BH), tear ferning (TF) and the participant's subjective symptoms, pre- and post-sleep. METHODS: Thirty subjects were recruited, consisting of two evenly distributed groups who were symptomatic of dry eye (DE) and those that were asymptomatic dry eye, determined using the McMonnies questionnaire. Subjects were evaluated at 10 p.m. (baseline), on waking at 7 a.m., and then hourly until 10 a.m. At each visit, tear meniscus height (TMH), various subjective factors, BH and POTF stability by non-invasive break-up time (NIBUT) were assessed. Tear collection was performed at 10 p.m, 7 and 10 a.m. for TF analysis. RESULTS: With the exception of burning, all other symptoms (comfort, dryness, clarity of vision, and grittiness) revealed an overnight change (p < 0.05) within each group, but not between the two groups (p > 0.05). Both the tear meniscus height and BH were elevated upon waking and differed significantly between test times for each group (p < 0.05), but not between groups (p > 0.05). NIBUT was lower for the DE group (p < 0.001). The non-dry eye (NDE) group did not significantly alter over time (p > 0.05), but the DE group did (p = 0.004), with a longer NIBUT in the morning. TF demonstrated a degraded pattern upon waking for both groups (p < 0.05). Most of the changes returned to baseline within an hour after waking. CONCLUSIONS: The properties of the POTF undergo a change during extended periods of eye closure and the human POTF is different upon waking to that present immediately before sleep. Most of the parameters determined rapidly revert to baseline levels once the POTF is allowed to refresh.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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".