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Record W2017971359 · doi:10.1097/mph.0b013e318257a13c

Usability Testing of a Smartphone for Accessing a Web-based e-Diary for Self-monitoring of Pain and Symptoms in Sickle Cell Disease

2012· article· en· W2017971359 on OpenAlexaff
Eufémia Jacob, Jennifer Stinson, Joana Duran, Ankur Gupta, Mário Gerla, Mary Ann Lewis, Lonnie K. Zeltzer

Bibliographic record

VenueJournal of Pediatric Hematology/Oncology · 2012
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicineIrritabilityFeelingUsabilityPallorPhysical therapyAnxietyDiseasePediatricsPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

We examined the usability of smartphones for accessing a web-based e-Diary for self-monitoring symptoms in children and adolescents with sickle cell disease (SCD). One group of participants (n = 10; mean age, 13.1 ± 2.4 y; 5 M; 5 F) responded to questions using precompleted paper-based measures. A second group (n = 21; mean age, 13.4 ± 2.4 y; 10 M; 11 F) responded based on pain and symptoms they experienced over the previous 12 hours. The e-Diary was completed with at least 80% accuracy when compared to paper-based measures. Symptoms experienced over the previous 12 hours included feeling tired (33.3%), headache (28.6%), coughing (23.8%), lack of energy/fatigue (19.0%), yellowing of the eyes (19.0%), pallor (19.0%), irritability (19.0%), stiffness in joints (19.0%), general weakness (14.3%), and pain (14.3%), rating on average as 2.0 ± 1.7 (on 0 to 10 scale). Overall, sleep was good (8.1 ± 1.4 on the 0 to 10 scale). In conclusion, children with SCD were able to use smartphones to access a web-based e-Diary for reporting pain and symptoms. Smartphones may improve self-reporting of symptoms and communication between patients and their health care providers, who may consequently be able to improve pain and symptom management in children and adolescents with SCD in a timely manner.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.167
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.020
GPT teacher head0.305
Teacher spread0.285 · 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 teacher head, 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

Citations83
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Pediatric Hematology/OncologySame topicHemoglobinopathies and Related DisordersFrench-language works237,207