<scp>CLEARED</scp> (Chemicals and Litmus testing with Effective Alkaline Range for Eye Damage): A Prospective, Interventional Study
Bibliographic record
Abstract
OBJECTIVES: While immediate diagnosis and irrigation is standard chemical eye burn practice, it is unknown to what extent specific pH measurements influence management, given the frequent clinical availability of narrow-spectrum nitrazine pH strips. We hypothesize that exclusive broad-spectrum pH strip implementation leads to more accurate measurement and expedited ophthalmologic consultation. METHODS: At a Level I trauma center over 25 months, all emergent adult ophthalmology consultations for chemical burns were included in a pre-intervention (n = 22) and post-intervention (n = 20) study design. During this time, narrow-spectrum nitrazine pH strips available to non-obstetric emergency department (ED) staff were exclusively replaced by broad-spectrum strips. Causative chemical, time from triage to ophthalmology consultation, examination findings, ocular pH by ED and ophthalmology staff, and irrigation quantity were analyzed. RESULTS: Most burns were alkaline. Time from triage (p = 0.043) and irrigation quantity following consultation (p = 0.047) each decreased following exclusive ED implementation of broad-spectrum pH strips. There was greater pH congruence between consulting and primary physicians after intervention (p = 0.03). CONCLUSIONS: Exclusive non-obstetric implementation of broad-spectrum pH strips may allow greater accuracy and faster management of ocular chemical burns. Availability of narrow-spectrum pH strips may be dangerous clinically by falsely reassuring the examiner with inherent inaccuracy.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".