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Record W2637046630 · doi:10.1080/09537104.2017.1327656

Platelet concentrates for bone regeneration: Current evidence and future challenges

2017· review· en· W2637046630 on OpenAlexaff
Zahi Badran, Mohamed‐Nur Abdallah, Jesús Torres, Faleh Tamimi

Bibliographic record

VenuePlatelets · 2017
Typereview
Languageen
FieldMedicine
TopicPeriodontal Regeneration and Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsPlateletRegeneration (biology)Platelet activationMedicineCell biologyChemistryInternal medicineBiology

Abstract

fetched live from OpenAlex

Activated platelet concentrates are autologous blood preparations containing supraphysiological concentration of platelets. Platelet concentrates are commonly used for bone regeneration purposes based on the fact that growth factors released from activated platelets alpha granules have osteoinductive effects on bone cells. Although most preclinical and clinical studies show that platelet concentrates improve the outcomes of bone regeneration procedures, some studies reported conflicting results and even negative effects on bone healing. Several confounding parameters have been suggested as possible reasons for such inconsistencies (i.e. preparation and activation methods). However, heterogeneity in clinical studies makes drawing evidence-based conclusions difficult. On the other hand, recent findings show that the constituents of platelets dense granules (i.e. serotonin, ATP, Ca2+) have potential inhibitory effects on bone metabolism. Accordingly, we suggest that a partial explanation for the conflicting results could be the potential negative effects that dense granules may have on 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.

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)0.002
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.326
GPT teacher head0.448
Teacher spread0.122 · 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

Citations62
Published2017
Admission routes1
Has abstractyes

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