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Record W2571440094 · doi:10.1182/blood.v112.11.905.905

Predictors of Osteopenia/Osteoporosis in Children with Acute Lymphoblastic Leukemia.

2008· article· en· W2571440094 on OpenAlexaff
Uma H. Athale, C. Webber, Ronald D. Barr

Bibliographic record

VenueBlood · 2008
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOsteopeniaMedicineOsteoporosisBone mineralPopulationPediatricsStandard scoreInternal medicineDual-energy X-ray absorptiometryPhysical therapy

Abstract

fetched live from OpenAlex

Abstract Background: Loss of bone mineral is a major problem in children with acute lymphoblastic leukemia (ALL), resulting in acute and chronic morbidity. About 30–40% of children with ALL will develop osteopenia/osteoporosis (OP) and about 10–15% will suffer from fractures. Identifying a population at high risk for OP is important to implementing a preventive strategy (e.g. biphosphonate therapy). So far predictors of OP in association with ALL in children are uncertain. Hence we undertook the following study to evaluate predictors of OP in children with ALL treated according to Dana-Farber Cancer Institute protocols. Aim: To evaluate the relationship between lumbar spine bone mineral density (LS-BMD) Z scores in patients with ALL during maintenance therapy and the variables of age at diagnosis (< 10 vs. ≥ 10-years), risk group [Standard (SR) vs. high-risk (HR)], gender (male vs. female) and LS-BMD at diagnosis. Methods: Children (≤ 18-years) diagnosed with ALL during the period 1995–2006 who were in first clinical remission, were included in the study. LS-BMD was measured using dual-energy X-ray absorptiometry (DEXA) at the time of diagnosis (n=88) and during the maintenance phase of therapy (n=119). The actual values of LS-BMD were expressed as age and gender matched Z-scores based on local population norms. Regression analyses were used to evaluate the risk of osteopenia, defined as LS-BMD Z score < -1.00, and osteoporosis, defined as LS-BMD Z score < -2.00. We evaluated the effect of age at diagnosis, gender, ALL risk category and LS-BMD at diagnosis on the LS-BMD during maintenance phase of therapy. Results: Of the 119 patients, 19 (16%) were ≥ 10-years of age, 46 (39%) were girls and 41 (34.5%) had HR ALL. At diagnosis 29 of 88 (33%) patients had osteopenia and 6 (6.8%) had osteoporosis whereas, during maintenance therapy, 47/119 patients (39.5%) had osteopenia and 10 (8.4%) patients had osteoporosis. LS-BMD at diagnosis had a positive linear relationship with LS-BMD during maintenance therapy (Pearson correlation coefficient 0.721, p<0.001). Older children and children with HR ALL had a significantly higher risk of osteopenia compared to younger children (p=0.01) and children with SR ALL (p=0.019). Age and risk category were confounding variables since all children ≥ 10 years were classified as HR ALL. Gender by itself had no significant effect. However, the effect of age on the LS-BMD during maintenance phase was gender-dependent with older girls having lower LS-BMD compared to older boys. Conclusions: Osteopenia and osteoporosis (OP) are common in children with ALL. Age over 10-years, female gender, HR ALL and lower LS-BMD at diagnosis are predictors of lower LS-BMD during the maintenance phase of therapy. Using these variables it is feasible to develop a predictive model to define the risk of OP during the maintenance phase of therapy. Larger prospective studies will better define this risk.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.255
Teacher spread0.243 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

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