Impact of Time Between Collection and Collection Method on Human Tear Fluid Osmolarity
Bibliographic record
Abstract
AIM: To generate data on the variability of tear osmolarity in a control (normal, non-dry eye) and symptomatic dry eye population (Ocular Surface Disease Index: OSDI ≥20). A secondary outcome is the determination of the effect that tear collection technique has on the osmolarity of the sample. MATERIALS AND METHODS: This was a two-phase study that recruited 20 subjects (n = 10 normal, n = 10 dry eye) to evaluate the influence of time between measurements (Phase I) and 30 subjects (n = 15 normal, n = 15 dry eye) to evaluate the influence of collection technique (Phase II). As part of Phase I, serial tear osmolarity measurements were performed on each eye; four separated by 15 min followed by four separated by 1 min, at each of three visits. Phase II compared the consecutive measurement of four in vivo tear samples to four in vitro measurements on tears collected and dispensed from a glass capillary tube. RESULTS: During Phase I, the dry eye group had a significantly higher maximum osmolarity (334.2 ± 25.6 mOsm/L) compared to the normal group (304.0 ± 8.4 mOsm/L, p = 0.002). No significant differences were observed whether collections were performed at 15 or 1 min intervals. During Phase II, the in vivo osmolarity was equivalent to in vitro measurements from glass capillary tube samples for both the dry eye group (323.0 ± 16.7 mOsm/L versus 317.7 ± 24.8, p = 0.496), and for the normal subjects (301.2 ± 7.2 mOsm/L versus 301.9 ± 16.0 mOsm/L, p = 0.884). CONCLUSION: Symptomatic dry eye subjects exhibited a significantly higher tear osmolarity and variation over time than observed in normal subjects, reflecting the inherent tear film instability of dry eye disease. There was no change in the distribution of tear osmolarity measurements whether tears were collected in rapid succession or given time to equilibrate, and collection method had no impact on tear osmolarity.
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 | 0.011 | 0.026 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".