MétaCan
Menu
Back to cohort

The Canadian Institute for Health Information Data Quality Framework, Version 1: A Meta-Evaluation and Future Directions.

2001· article· en· W17084866 on OpenAlexaboutno aff
Jennifer Long, Justin Richards, Craig Seko

Bibliographic record

VenueIQ · 2001
Typearticle
Languageen
FieldHealth Professions
TopicMedical Coding and Health Information
Canadian institutionsnot available
Fundersnot available
KeywordsData qualityQuality (philosophy)Computer scienceData scienceHealth informationPolitical scienceHealth careEngineeringOperations management

Abstract

fetched live from OpenAlex

Serotonin (5-HT) and 5-HT agonists have various resetting effects on the master clock, located in the suprachiasmatic nucleus (SCN), depending on the species. In rats, they induce photic-like effects on both locomotor activity rhythms and gene expression in the SCN. The 5-HT receptor(s) mediating these effects at circadian time 22 are localized in the SCN, most likely at a presynaptic level, on the retinohypothalamic terminals (RHT) known to convey photic information by releasing glutamate. Indeed, RHT degeneration blocks photic-like effects of a non-specific 5-HT agonist, quipazine. However, the 5-HT receptor subtype(s) involved is still unknown, although 5-HT(3) receptor activation is known to induce glutamate release. We thus analyzed the effects of selective 5-HT(3) agonist and antagonist, as well as a specific NMDA receptor antagonist, on different parameters of the clock. This study shows that the 5-HT(3) receptor mediates the resetting effects of quipazine on locomotor activity rhythms. The 5-HT(3) receptor is only partially implicated in quipazine-induced expression of c-FOS, while NMDA receptor inhibition blocks quipazine photic-like effects on both parameters. Taken together, photic-like responses produced by 5-HT stimulation in rats are likely mediated by (presynaptic?) 5-HT(3) receptor activation followed by NMDA receptor activation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.752
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.566
GPT teacher head0.564
Teacher spread0.002 · 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 teacher head, not a consensus.

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

Citations0
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueIQSame topicMedical Coding and Health InformationFrench-language works237,207