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Record W2617852027 · doi:10.1007/s00784-017-2133-z

Use of platelet-rich fibrin in regenerative dentistry: a systematic review

2017· review· en· W2617852027 on OpenAlexaff
Richard J. Miron, Giovanni Zucchelli, Michael A. Pikos, Maurice Salama, Samuel Lee, Vincent Guillemette, Masako Fujioka‐Kobayashi, Mark Bishara, Yufeng Zhang, Hom‐Lay Wang, Fatiha Chandad, Cleopatra Nacopoulos, Alain Simonpieri, Alexandre Amir Aalam, Pietro Felice, Gilberto Sammartino, Shahram Ghanaati, Maria Hernandez, Joseph Choukroun

Bibliographic record

VenueClinical Oral Investigations · 2017
Typereview
Languageen
FieldMedicine
TopicPeriodontal Regeneration and Treatments
Canadian institutionsUniversité Laval
FundersNova Southeastern University
KeywordsPlatelet-rich fibrinMedicinePeriodontologyDentistryHard tissueGingival recessionRandomized controlled trialSoft tissueClinical trialRegenerative medicineFibrinOrthodonticsSurgeryPathology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.003
metaresearch head score (Gemma)0.012
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 score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0060.007
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.647
GPT teacher head0.546
Teacher spread0.101 · 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

Citations523
Published2017
Admission routes1
Has abstractno

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