Pupil Size and Reactivity in Pediatric Patients With Sickle Cell Disease
Bibliographic record
Abstract
Pupil size and reactivity have been studied to objectively measure pain utilizing pupillometry measurements. Given the challenges associated with treating vaso-occlusive pain in pediatric patients with sickle cell disease, better assessment tools are needed. The objective of this study is to establish normative values for pupil size and reactivity in pediatric patients with sickle cell disease with the hope that pupillometry can be used as a tool to objectively measure pain and response to treatment with analgesic medications. Readings were performed using a NeurOptics PLR-2000 pupillometer. Forty-four males and 38 females, all black, were studied. Their median age was 11 years (range: 2 to 21). When comparing our participants with white participants in a previously published pediatric study, there was a significant difference in maximum constriction velocity ( t =3.45, P =0.009), maximum pupil size ( t =-5.57 mm, P <0.0001), and minimum pupil size ( t =-3.24, P =0.002). There was no significant difference in pupil size and reactivity between patients with sickle cell disease and black patients without the disease when compared with the previously published study. Therefore, further investigation of pupillometry within the black population during vaso-occlusive crisis and in the "well state" is warranted in pediatric patients with sickle cell disease.
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".