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Record W3173858832 · doi:10.3390/children8070559

Parental Engagement of a Prototype Electronic Diary in an Ambulatory Setting Following Adenotonsillectomy in Children: A Prospective Cohort Study

2021· article· en· W3173858832 on OpenAlexaff
Tobial McHugh, Karen A. Brown, Sam J. Daniel, Sharmila Balram, Chantal Frigon

Bibliographic record

VenueChildren · 2021
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsMcGill UniversityMontreal Children's Hospital
Fundersnot available
KeywordsAmbulatoryMedicineProspective cohort studyPhysical therapySurgery

Abstract

fetched live from OpenAlex

Adenotonsillectomy is performed in children on an outpatient basis, and pain is managed by parents. A pain diary would facilitate pain management in the ambulatory setting. Our objective was to evaluate the parental response rate and the compliance of a prototype electronic pain diary (e-diary) with cloud storage in children aged 2–12 years recovering from adenotonsillectomy and to compare the e-diary with a paper diary (p-diary). Parents recorded pain scores twice daily in a pain diary for 2 weeks post-operation. Parents were given the choice of an e-diary or p-diary with picture message. A total of 208 patients were recruited, of which 35 parents (16.8%) chose the e-diary. Most parents (98%) chose to be contacted by text message. Eighty-one families (47%) returned p-diaries to us by mail. However, the response rate increased to 77% and was similar to that of the e-diary (80%) when we included data texted to the research phone from 53 families. The proportion of diaries with Complete (e-diary:0.37 vs. p-diary:0.4) and Incomplete (e-diary:0.43 vs. p-diary:0.38) data entries were similar. E-diaries provide a means to follow patients in real time after discharge. Our findings suggest that a smartphone-based medical health application coupled with a cloud would meet the needs of families and health care providers alike.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.274
Teacher spread0.267 · 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.

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

Citations2
Published2021
Admission routes1
Has abstractyes

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