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Record W4200419850 · doi:10.1016/j.cpnec.2021.100108

The Cortisol Assessment List (CoAL) A tool to systematically document and evaluate cortisol assessment in blood, urine and saliva

2021· review· en· W4200419850 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComprehensive Psychoneuroendocrinology · 2021
Typereview
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsReliability (semiconductor)WeightingPsychologyCoalQuality (philosophy)Applied psychologyTraitClinical psychologyStatisticsComputer scienceEngineeringMathematicsMedicine

Abstract

fetched live from OpenAlex

Background: The reliable assessment of cortisol is a necessary requirement to produce replicable research. Several recommendations to increase cortisol assessment reliability exist. However, cortisol assessment methodology is still rather heterogeneous. For this reason, the Cortisol Assessment List (CoAL) was created.The CoAL can be used to guide researchers during the planning phase and document which measures were taken to increase cortisol data reliability in original studies. Moreover, the CoAL can be used to evaluate data quality in meta research. The items representing strategies to obtain reliable cortisol data can be weighted to indicate which are absolutely necessary to consider and which could be applied less restrictively in order to balance data quality and feasibility. In this paper, the construction process of the CoAL is described. Methods: Item synthesis of the CoAL included a literature search to extract empirically based suggestions regarding the reliable assessment of cortisol. Estimates for the item weighting system were obtained by inviting experts in the field to participate in an online survey (n = 25). Inter-rater reliability (IRR) of the CoAL, was determined by letting independent raters use the CoAL to evaluate a set of randomly selected original studies (k = 90). Results: (Cortisol Awakening Response (CAR): 52%; basal cortisol: 52%; reactive cortisol: 44%) in order to obtain reliable cortisol data. Inter-rater agreement was very high (Cohen's Kappa = .98 - 0.99), indicating sufficient psychometric quality of the CoAL. Discussion: The CoAL is the first tool to systematically plan, document and evaluate cortisol assessment. The survey results indicate that the majority of respondents are aware of essential requirements to increase data reliability. However, results were heterogeneous for some items, highlighting the need to start a process of developing a broad scientific consensus regarding reliable cortisol assessment. The implementation of the CoAL could be a first step in this direction. In conclusion, the CoAL reflects empirical evidence and expert knowledge regarding cortisol assessment and can be used as a flexible tool to plan and document empirical studies or evaluate cortisol data quality in meta research.

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.954
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.001
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 head0.072
GPT teacher head0.404
Teacher spread0.332 · 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