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Record W4200241389 · doi:10.1097/mph.0000000000002387

Pupil Size and Reactivity in Pediatric Patients With Sickle Cell Disease

2021· article· en· W4200241389 on OpenAlexaff

Bibliographic record

VenueJournal of Pediatric Hematology/Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsPediatric Oncology Group
Fundersnot available
KeywordsPupillometryPupil sizePupilPupil diameterDiseasePopulationVaso-occlusive crisisSickle cell anemia

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.012
GPT teacher head0.278
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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