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Record W4366277802 · doi:10.3390/jcm12082933

Should Trazodone Be First-Line Therapy for Insomnia? A Clinical Suitability Appraisal

2023· review· en· W4366277802 on OpenAlexaff
Rafael Pelayo, Suzanne M. Bertisch, Charles M. Morin, John W. Winkelman, Phyllis C. Zee, Andrew D. Krystal

Bibliographic record

VenueJournal of Clinical Medicine · 2023
Typereview
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversité Laval
FundersIdorsia Pharmaceuticals
KeywordsMedicineTrazodoneStatement (logic)Medical prescriptionPanel discussionPsychiatryFamily medicineNursingAntidepressantAnxiety

Abstract

fetched live from OpenAlex

Trazodone is one of the most commonly used prescription medications for insomnia; however, some recent clinical guidelines do not recommend its use for treating insomnia. This clinical appraisal critically reviews the scientific literature on trazodone as a first-line treatment for insomnia, with the focus statement “Trazodone should never be used as a first-line medication for insomnia.” In addition, field surveys were sent to practicing physicians, psychiatrists, and sleep specialists to assess general support for this statement. Subsequently, a meeting with a seven-member panel of key opinion leaders was held to discuss published evidence in support and against the statement. This paper reports on the evidence review, the panel discussion, and the panel’s and healthcare professionals’ ratings of the statement’s acceptability. While the majority of field survey responders disagreed with the statement, the majority of panel members agreed with the statement based on the limited published evidence supporting trazodone as a first-line agent as they understood the term “first-line agent”.

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)0.017
metaresearch head score (Gemma)0.049
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.049
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0050.004
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.547
GPT teacher head0.625
Teacher spread0.078 · 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
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

Citations18
Published2023
Admission routes1
Has abstractyes

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