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Record W4293553353 · doi:10.14740/jh1038

Treatment of Light Chain Deposition Disease: A Systematic Review

2022· review· en· W4293553353 on OpenAlexvenueno aff
Adeel Masood, Hamid Ehsan, Qamar Iqbal, Ahmed Salman, Hamza Hashmi

Bibliographic record

VenueJournal of Hematology · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAmyloidosis: Diagnosis, Treatment, Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBortezomibStem cellDiseaseChemotherapyRetrospective cohort studyAutologous stem-cell transplantationOncologyInternal medicineIntensive care medicineSurgeryMultiple myeloma

Abstract

fetched live from OpenAlex

Light chain deposition disease (LCDD) is a rare hematologic disorder that can affect any organ but predominantly involves the kidneys. Existing literature is limited to case reports and small single-center retrospective series, explaining the lack of any treatment algorithms and management guidelines for patients with this disorder. In this systematic review of literature, we explored the role of standard and high-dose chemotherapy-autologous stem cell transplant for LCDD. A total of 11 studies were identified to evaluate the hematologic and renal responses to various treatment regimens. Autologous stem cell transplant and bortezomib-based regimens appear to have reasonable safety and efficacy for this rare hematologic disorder, albeit some statistical and analytical limitations. Large multicenter retrospective and prospective studies are needed to better elucidate the role of various chemotherapy regimens as well as autologous stem cell transplant for patients with LCDD.

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)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.024
GPT teacher head0.327
Teacher spread0.303 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations21
Published2022
Admission routes1
Has abstractyes

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