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Record W4389219823 · doi:10.1182/blood-2023-187067

Evaluation of Bleeding Self-Assessments By Patients with Immune Thrombocytopenia (ITP): An Agreement Study

2023· article· en· W4389219823 on OpenAlexaff
Bianca Clerici, Ngan Tang, Madison Cranstone, Yang Liu, Milena Hadzi‐Tosev, Joanne Nixon, Melanie St John, Maryam Shirinzadeh, Erin Jamula, John G. Kelton, Donald M. Arnold

Bibliographic record

VenueBlood · 2023
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineCohen's kappaKappaInter-rater reliabilityGastrointestinal bleedingImmune thrombocytopeniaSurgeryPediatricsInternal medicineRating scalePlatelet

Abstract

fetched live from OpenAlex

Introduction. Immune thrombocytopenia (ITP) is an autoimmune condition that causes an increase in the risk of bleeding. Bleeding is a patient-important outcome; however, timely and complete assessments of bleeding are time- and labour-intensive. ITP bleeding measurements may be simplified with patient self-assessments. We designed this study to compare the agreement of bleeding assessments done by ITP patients and by trained research staff. Methods. All patients were identified from the McMaster ITP Registry, a longitudinal registry study of consecutive adult patients with thrombocytopenia followed at the McMaster University Medical Centre, a tertiary referral clinic. A modified version of the ITP Bleeding Scale was used for all bleeding assessments, which captured the patients' worst bleeding event at each of 9 anatomical sites - skin, mouth, epistaxis, gastrointestinal, genitourinary, gynecological, pulmonary, ocular or intracranial - graded from 0 (no bleeding) to 2 (severe bleeding) from the time of the last assessment (typically 6 months prior). Patients were provided with instructions on how to use the scale and asked to complete bleeding self-assessments using an online tool. Once the patients completed their self-assessments, a trained research staff member contacted the patient to repeat the bleeding assessment by telephone. Chance-corrected interrater agreement was determined using the kappa statistic for 2-way agreement (Grade 2 vs. Grade 0 or 1 bleeding) and for 3-way agreement (Grade 0 vs. Grade 1 vs. Grade 2). Chance-independent 2-way agreement was also measured using the phi statistic. The primary analysis was the 2-way kappa, since the detection of Grade 2 bleeds is clinically important. Results. We enrolled 108 consecutive patients with ITP from the McMaster ITP Registry who had duplicate bleeding assessments done. The median time between assessments was 3 days (IQR, 2-5). Median age of patients in the study was 53 years (IQR, 38-64), 64% were female. The worst bleeding events, as determined by research staff, were Grade 0 (n=22, 20.4%), Grade 1 (n=34, 31.5%) or Grade 2 (n=52, 48.1%). There was perfect agreement for bleeding assessments at all anatomical sites for 44 patients (40.7%). There were no intracranial hemorrhages and no Grade 2 genitourinary or pulmonary bleeds were reported. Chance-corrected 2-way agreement was excellent for gynecological (k=0.86, 95% CI 0.71-1.02), gastrointestinal (k=1), genitourinary (k=1), pulmonary (k=1) and intracranial (k=1) bleeds ( Table). Agreement was good for skin (k=0.68, 95% CI, 0.54-0.82), oral (k=0.76, 95% CI, 0.53-0.98) and ocular (k=0.66, 95% CI, 0.04-1.28) bleeds, and moderate for epistaxis (k=0.58, 95% CI, 0.21-0.95). Results of chance-independent agreement and 3-way agreement were similar. Conclusions. Bleeding self-assessments by ITP patients yielded comparable results to trained research staff. Agreement for skin and epistaxis was lower than for other sites, suggesting that additional instructions or prompts may be needed for these categories. Bleeding self-assessments could simplify data collection in research and in clinical practice.

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)0.035
metaresearch head score (Gemma)0.088
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 score0.035
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.088
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
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 head0.028
GPT teacher head0.327
Teacher spread0.299 · 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
Published2023
Admission routes1
Has abstractyes

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