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Record W2333993500 · doi:10.1097/bco.0000000000000207

Biologics in treating shoulder disease

2015· article· en· W2333993500 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Orthopaedic Practice · 2015
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsWestern UniversitySt Joseph's Health Care
Fundersnot available
KeywordsMedicineEnthesisMesenchymal stem cellRegeneration (biology)Rotator cuffTendonBone healingSurgeryBioinformaticsPathologyCell biology

Abstract

fetched live from OpenAlex

Rotator cuff repair healing remains a significant clinical challenge despite technical advances in minimally invasive surgical repair. There is an unmet need for strategies to augment the repair construct by biologically enhancing the intrinsic healing potential of the tendon while mechanically protecting the healing enthesis during the immediate postoperative period. Platelet concentrates, scaffolds, and mesenchymal stem cells each hold promise for improving the healing rate and induce the regeneration of functional tissues. These strategies can enhance cell recruitment, proliferation, and differentiation, as well as provide a structural microenvironment for host cells through their three-dimensional configuration. Despite these potential benefits, there is currently limited clinical evidence supporting their efficacy in-vivo to improve structural healing rates and functional outcomes. Future work in this field is necessary to better understand the mechanism of action, appropriate indications, and favored methods of delivery for biological augments to tendon-bone healing.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.894
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.120
GPT teacher head0.415
Teacher spread0.295 · 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