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Record W4248230300 · doi:10.1111/acem.13170

In Reply

2017· letter· en· W4248230300 on OpenAlexaff
Catherine Varner

Bibliographic record

VenueAcademic Emergency Medicine · 2017
Typeletter
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsSchwartz/Reisman Emergency Medicine InstituteMount Sinai Hospital
Fundersnot available
KeywordsMedicineMoodEmergency departmentNeuropsychologyCognitionRandomized controlled trialClinical psychologyPopulationTest (biology)Neuropsychological assessmentPsychiatry

Abstract

fetched live from OpenAlex

We thank the journal for the opportunity to respond to the recent comments made regarding our article, “Cognitive Rest and Graduated Return to Usual Activities Versus Usual Care for Mild Traumatic Brain Injury: A Randomized Controlled Trial of Emergency Department Discharge Instructions.”1 From the submitted comments, it appears the author suggests that reduced cognitive performance, health status, and/or mood problems may have impacted the participants’ ability to accurately respond to the Postconcussion Symptom Score (PCSS) questions. The author also suggests that by using the PCSS questionnaire, possible deficits in cognitive and emotional domains are left unexplored in our study. However, we wish to point out that the validated, 22-item PCSS questionnaire includes emotional, somatic, and cognitive domains and has been extensively studied in this patient population. Additionally, self-reported PCSS has been significantly correlated with cognitive test performance, as well as functional MRI.2, 3 We agree that neuropsychological testing would have contributed significant information to participant assessment; however, we were conducting a pragmatic trial in the emergency department (ED), where baseline neuropsychological status (i.e., before the injury) would likely be unknown. Future studies in this area should attempt to include more objective measures such as balance and cognitive testing, during both the index ED visit and the participant follow-up. The second concern raised by the author is the possibility that unmeasured factors may have caused nondifferentiation between groups. As in any clinical study, unmeasured variables may potentially bias study results, particularly if these factors impact group allocation. To minimize baseline differences between the treatment and control groups, we randomized treatment allocation. Randomization, if done properly, should make both groups similar in terms of the distribution of both known and unknown prognostic baseline factors that could confound or influence outcomes.4 We are confident that our blinded randomization process was upheld and that our intention-to-treat analysis strategy was robust so any unmeasured differences between groups would have been due to chance variation.

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)0.007
metaresearch head score (Gemma)0.083
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.083
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0050.005
Open science0.0030.003
Research integrity0.0200.032
Insufficient payload (model declined to judge)0.0590.038

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 head0.076
GPT teacher head0.367
Teacher spread0.291 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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