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Record W4224325150 · doi:10.1097/mph.0000000000002465

Integration of Patient-reported Outcome Measures in Pediatric Hematology: A Qualitative Methods Study

2022· article· en· W4224325150 on OpenAlexaffabout

Bibliographic record

VenueJournal of Pediatric Hematology/Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversité de MontréalChildren's Hospital of Eastern OntarioUniversity of OttawaUniversité LavalCegep regional de Lanaudiere
Fundersnot available
KeywordsPromHaemophiliaPatient-reported outcomeQuality of life (healthcare)Focus groupQualitative researchHealth careMEDLINEImmune thrombocytopenia

Abstract

fetched live from OpenAlex

Patient-reported outcome measures (PROMs) are self-reported questionnaires that allow patients and families to evaluate health-related experiences without influence or oversight from health care professionals. This study aimed to rate the relevance of existing PROMs for pediatric hematology patients, as identified by a recent systematic review, as well as to evaluate the receptivity of electronic PROM integration into clinical practice. Focus groups and interviews were conducted with children (10) and parents (19) impacted by nonmalignant hematological disorders, as well as with health care professionals (6). We observed strong support for the TranQol in thalassemia (100% for both parents [P] and children [C]); the Canadian Haemophilia Outcomes-Kids' Life Assessment Tool (CHO-KLAT) (100% P, 75% C) and Haemophilia Quality of Life questionnaire (Haemo-QoL) (100% P and C) in hemophilia; the Pediatric Quality of Life Inventory (PedsQL) Sickle Cell Module (75% P, 100% C); and the Kids ITP Tool in immune thrombocytopenia (100% P, 66.7% C). Generic tools such as the PedsQL Generic were met with mixed support. Electronic PROM integration received universal support. We obtained strong support for the integration of a web-based platform into clinical practice and a preference for disease-specific PROMs over generic PROMs. Future projects may explore the development of a child-friendly Canadian web-based platform to standardize quality-of-life evaluation within the clinical encounter.

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)0.058
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.307

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0580.059
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.153
GPT teacher head0.492
Teacher spread0.339 · 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 designQualitative
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
Published2022
Admission routes2
Has abstractyes

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