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Record W2917175365 · doi:10.14245/ns.2121077.075

Commentary on “Hemodynamic Management of Acute Spinal Cord Injury”

2021· letter· en· W2917175365 on OpenAlexaff
Nader Hejrati, Michael G. Fehlings

Bibliographic record

VenueNeurospine · 2021
Typeletter
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsHemodynamicsSpinal cord injuryMedicineSpinal cordAnesthesiaPsychiatry

Abstract

fetched live from OpenAlex

The review article by Lee et al. 1 addresses a topic of significant importance in the extensively multifaceted management of patients suffering from acute spinal cord injuries (SCIs). It has been almost three decades since the term secondary injury was recognized as the most crucial pathophysiological process influencing clinical outcomes in the postinjury period. 2 Ischemia due to compromised spinal cord perfusion was shown to be one of the major components of the secondary injury cascade. This finding led to one of the most essential components of acute SCI management, in that adequate spinal cord perfusion shall be maintained through avoidance of systemic hypotension and support of mean arterial pressure (MAP).

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)0.022
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.040
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0020.004
Open science0.0030.001
Research integrity0.0400.028
Insufficient payload (model declined to judge)0.0060.009

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 head0.042
GPT teacher head0.379
Teacher spread0.337 · 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
GenreCommentary

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

Citations4
Published2021
Admission routes1
Has abstractyes

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