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Development of an adverse outcome pathway for deposition of energy leading to bone loss.

2023· preprint· en· W4376611084 on OpenAlexaff
Snehpal Sandhu, Mitchell Keyworth, Syna Karimi‐Jashni, Dalya Alomar, Benjamin Smith, Tatiana Kozbenko, Stephen B. Doty, Robyn Hocking, Nobuyuki Hamada, Robert F. Reynolds, Ryan T. Scott, Sylvain V. Costes, Afshin Beheshti, Carole L. Yauk, Ruth C. Wilkins, Vinita Chauhan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversity of OttawaHealth Canada
Fundersnot available
KeywordsAdverse Outcome PathwayOsteoporosisAdverse effectConcordanceCausal inferenceMedicineInternal medicinePathologyBiologyComputational biology

Abstract

fetched live from OpenAlex

Bone loss, commonly seen in osteoporosis, is a condition that entails a progressive decline of bone mineral density and microarchitecture, often seen in post-menopausal women. Bone loss has been widely reported in astronauts exposed to a plethora of stressors and in patients with osteoporosis following radiotherapy for cancer. Studies on mechanisms are well documented but the causal connectivity of events to bone loss development remains incompletely understood. Herein, the adverse outcome pathway (AOP) framework was used to organize data and develop a qualitative AOP beginning from deposition of energy (the molecular initiating event) to bone loss (the adverse outcome). A literature review was conducted to compile and evaluate the state of knowledge based on the modified Bradford Hill criteria. Following review of 1865 studies, an empirically supported AOP was developed, showing the progression to bone loss through many factors affecting the activities of bone-forming osteoblasts and bone-resorbing osteoclasts. The structural, functional, and quantitative basis of each proposed relationship was defined, for inference of causal changes between key events. Current knowledge and its gaps relating to dose-, time- and incidence-concordance across the key events were identified, as well as modulating factors that influence linkages. The new priorities for research informed by the AOP highlight areas for improvement to enable development of a quantitative AOP used to support risk assessment strategies for space travel or cancer radiotherapy.

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)0.010
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.006
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.108
GPT teacher head0.395
Teacher spread0.287 · 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 designTheoretical or conceptual
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
Published2023
Admission routes1
Has abstractyes

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