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Record W2492453327 · doi:10.1017/cbo9780511545795.018

Biochemical markers of bone turnover

2000· book-chapter· es· W2492453327 on OpenAlexaff
David A. Hanley

Bibliographic record

VenueCambridge University Press eBooks · 2000
Typebook-chapter
Languagees
FieldMedicine
TopicBone health and treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsBone remodelingBone resorptionBone cellBone matrixBone formationCell biologyBone remodeling periodResorptionChemistryMatrix (chemical analysis)EndocrinologyBiochemistryBiologyOsteoblastAnatomyIn vitro

Abstract

fetched live from OpenAlex

The 1980s and 1990s have seen major advances in the understanding of regulation of bone metabolism. The section on cellular and hormonal environment of bone reviews the basics of biochemistry and cell biology of bone. With the elucidation of the synthesis and post-translational modification of bone collagen, has come the ability to measure markers of both collagen synthesis and its breakdown. In the formation of new bone, and the breakdown or resorption of old bone, components of the non-collagen matrix of bone are also released by the cells that are synthesizing these products or remodeling the bone matrix. Because these components are released from bone into the circulation, their measurement may provide a window for clinical assessment of the process of bone resorption and formation. The normal adult human skeleton is constantly remodeling. This is illustrated diagrammatically in Fig. 17.1. The skeleton may be regarded as being made up of millions of basic multicellular units (BMUs) or bone remodeling units (abbreviated in some publications as BRU). At any given time, most BMUs are in a resting stage. In a response to a variety of stimuli (mechanical stress, parathyroid hormone, withdrawal of estrogen, local release of growth factors and cytokines, etc.) a resting BMU can be stimulated into activity.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.007

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 head0.018
GPT teacher head0.222
Teacher spread0.205 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations1
Published2000
Admission routes1
Has abstractyes

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Same venueCambridge University Press eBooks→Same topicBone health and treatments→French-language works237,207→