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Record W2604365406 · doi:10.1111/apa.13859

Using a standardised protocol was effective in reducing hospitalisation and treatment use in children with newly diagnosed immune thrombocytopenia

2017· article· en· W2604365406 on OpenAlexaff
Roxane Labrosse, Mélanie Vincent, U‐P Nguyen, Caroline Chartrand, L Di Liddo, Yves Pastore

Bibliographic record

VenueActa Paediatrica · 2017
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicinePrednisoneImmune thrombocytopeniaPediatricsRetrospective cohort studyInternal medicinePlatelet

Abstract

fetched live from OpenAlex

AIM: Childhood immune thrombocytopenia (ITP) has been associated with low bleeding rates and a high frequency of spontaneous remission. Although current guidelines suggest that most patients are just observed, children still receive platelet-enhancing therapies for fear of bleeding complications. We hypothesised that a standardised protocol with a step-down approach would reduce hospitalisation and treatment use. METHOD: A retrospective chart review was performed on patients diagnosed with acute ITP between January 2010 and December 2014, before (n = 54) and after (n = 37) the standardised protocol, which was introduced in January 2013. Management and events during the first 3 months following diagnosis were recorded. RESULTS: The protocol resulted in a 34% decrease in the hospitalisation rate (p < 0.001) at diagnosis. Prednisone treatment duration at diagnosis was also significantly reduced (13.1 versus 5.8 days, p = 0.004). Children over 3 years of age were 3.8 times less likely to be hospitalised (95% CI 1.94-7.61) and 2.3 times less likely to receive treatment (95% CI 1.2-4.3). There was no difference in the rate of persistent ITP (38% versus 30%, p = 0.43) or serious bleeding complications (7% versus 5%, p = 0.70). CONCLUSION: Our ITP management protocol significantly reduced hospitalisation rates and length of prednisone treatment without any increase in disease complications.

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 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 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
Bibliometrics0.0000.000
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 head0.301
Teacher spread0.281 · 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

Citations11
Published2017
Admission routes1
Has abstractyes

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