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Validation of the Nordic Scoring System for Erythropoietic Stimulating Agents in MDS Using IWG 2006 Erythroid Response Criteria

2012· article· en· W2550933491 on OpenAlexaff
Jennifer Jayakar, Richard A. Wells, Dina Khalaf, Alex Mamedov, Adam Lam, Martha Lenis, Rena Buckstein

Bibliographic record

VenueBlood · 2012
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineInternational Prognostic Scoring SystemErythropoietinInternal medicinePopulationMedical recordMyelodysplastic syndromes

Abstract

fetched live from OpenAlex

Abstract Abstract 1721 The Nordic scoring system is commonly used to predict response to ESA's in MDS patients and is comprised of two weighted elements, patient's endogenous erythropoietin levels and transfusion dependency status, and three predictive categories of ESA response rates (E. Hellstrom-Lindberg et al., BJH 1997). We set out to validate the Nordic scoring system using the modified IWG erythroid response criteria for MDS 2006 (BD Cheson et al. Blood 2006) and identify any other clinical elements of prognostic importance. Methods: We conducted a retrospective review of 135 patients in a prospectively maintained MDS database of 400+ patients enrolled between the years of 2005 and 2012. Patients flagged as having had previous ESAs were included and categorized as ESA naive erythropoietin (EP), ESA naïve darbepoetin (DP), ESA non-naive EP and ESA non-naive DP. Nordic scores were calculated (when possible) using patient transfusion status and erythropoietin levels preceding ESA use, and actual response rates were determined using electronic medical records and transfusion histories. We recorded doses and schedules and evaluated the impact of WHO classification (3 groups) and IPSS risk score (L, Int-1 and Int-2) on overall response as well. We excluded patients who converted to AML during ESA trial, Jehovah witnesses and those with high risk MDS. We did not calculate Nordic scores in patients concurrently on ESA when referred to our center. Results: The patient population had a median age of 76 with 59% male. Out of 135 patients, 90 were put on EP, 35 put on DP and 10 put on both, with DP following EP in the 10 patients. Starting dose for most EP patients (59%) was 40,000 units q week with escalation to 60,000 units in non/suboptimal responders. GCSF was added concurrently in 36 (27%) patients. Starting dose for 71% of DP patients was 500 ug q3 weeks with increase to Q2 weeks for suboptimal response. The Nordic score was ‘calculatable’ in 109 patients pre ESA. Table 1 summarizes the response rates by Nordic score, IPSS transfusion dependence and type of ESA. Figures 1 and 2 depict response rates by ESA exposure and by WHO categories 1–3(see legend). We were not able to accurately document response durations. Conclusions: The Nordic scoring system is still valid for predicting response to ESA using IWG erythroid response criteria 2006 with slightly lower response rates at the highest score of > +1 than previously reported of 74%. We observed higher erythroid response rates in EP treated (42%) versus DP treated (31%) patients even after adjusting for Nordic scores. As expected, transfusion dependent MDS patients had lower responses to ESA than those independent of transfusions. Disclosures: Wells: Alexion: Honoraria, Research Funding; Janssen Ortho: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Buckstein:Celgene: Honoraria, Research Funding.

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)0.008
metaresearch head score (Gemma)0.015
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 score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.039
GPT teacher head0.316
Teacher spread0.278 · 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".

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Citations2
Published2012
Admission routes1
Has abstractyes

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