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Record W6991575350

Healthcare Provider Perceptions of Clinically Important Bleeding in Hematological Malignancies: A Qualitative Study

2023· dissertation· en· W6991575350 on OpenAlexaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2023
Typedissertation
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsnot available
Fundersnot available
KeywordsQualitative researchBleedHealth careClinical trialMEDLINEClinical significanceScale (ratio)Clinical Practice
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Acute leukemia (AL) is a rapidly progressive disease. AL and induction chemotherapy lead to an increased risk of bleeding. Bleeding is measured in clinical trials using the World Health Organization (WHO) bleeding scale. The scale defines a clinically significant bleed as a composite outcome of a grade 2 bleed or higher. The use of this composite outcome is problematic, as it does not distinguish minor bleeds, signs or symptoms of bleeding, does not consider the total burden of bleeding and lacks input from healthcare providers and patients. Given this, our objective was to identify healthcare providers' perspectives on the components of clinically important bleeding in AL patients. Methods: Using qualitative description, we conducted 19 interviews with physicians (n=12), nurses (n=3), and nurse practitioners (n=4) who provide care to AL patients undergoing induction chemotherapy in Canada. Participants were recruited from professional organizations, networks, and social media. Interview data were analyzed using an inductive approach for conventional content analysis. Results: Healthcare providers identified various factors that were considered to determine the significance or severity of a bleed. Participants assessed factors including the location and amount of blood, the management strategy, the need for intervention, multiple bleeds, changes in vital signs and other patient-specific factors. We developed three categories to differentiate bleeds: those with clinical significance, those with potential for clinical significance, and those without clinical significance. Conclusion: Healthcare providers considered various characteristics when determining the significance and or severity of a bleed. These characteristics were assessed in conjunction with other factors such as the patient's medical condition, bleeding history, and clinical intuition to predict the likelihood of a serious bleed. Future research should explore AL patients’ perspectives of clinically important bleeding to create a definition that is informed by evidence, clinicians, and patients.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0110.000

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 head0.051
GPT teacher head0.360
Teacher spread0.309 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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