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Record W2990233679 · doi:10.1016/j.arthro.2019.08.020

<i>Editorial Commentary:</i> Dermal Allograft: A Viable Allograft for Salvage Procedures in Treating Irreparable Rotator Cuff Tears

2019· editorial· en· W2990233679 on OpenAlexaff
Anjaneyulu Purnachandra Tejaswi Ravipati, Ivan Wong

Bibliographic record

VenueArthroscopy The Journal of Arthroscopic and Related Surgery · 2019
Typeeditorial
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsDalhousie University
Fundersnot available
KeywordsRotator cuffTearsMedicineCuffSurgery

Abstract

fetched live from OpenAlex

Our experience with superior capsule reconstruction (SCR) has been successful in patients with isolated, irreparable, supraspinatus tears; however, we have found that bridging reconstruction may have a better role in treating patients with some cuff remnant. Our results are promising, and, although there is new evidence to show that dermal allografts can heal in the setting of rotator cuff deficiency, the basic principle of restoring anatomy should not be ignored. SCR has been accepted as a salvage procedure for irreparable cuff tears, with the precise indications being elucidated. Reconnecting viable cuff muscle to tuberosity directly or through a graft should be considered before SCR.

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)0.003
metaresearch head score (Gemma)0.017
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.020
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0050.004
Open science0.0030.001
Research integrity0.0200.021
Insufficient payload (model declined to judge)0.0080.009

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 head0.008
GPT teacher head0.284
Teacher spread0.277 · 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
GenreEditorial

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

Citations0
Published2019
Admission routes1
Has abstractyes

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