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Record W4390193354 · doi:10.1002/alz.079997

Mapping of validated apathy scales onto the apathy diagnostic criteria for neurocognitive disorders

2023· article· en· W4390193354 on OpenAlexaff
Kritleen K. Bawa, Krushnaa Sankhe, Daniel R. Bateman, Jeffrey L. Cummings, Larry Ereshefsky, Masud Husain, Zahinoor Ismail, Valéria Manera, Jacobo Mintzer, Hans J. Moebius, Moyra E. Mortby, Anton P. Porsteinsson, Philippe Robert, David S. Miller, Krista L. Lanctôt

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsToronto Dementia Research AllianceUniversity of TorontoUniversity Health NetworkHealth Sciences CentreSunnybrook HospitalHotchkiss Brain InstituteUniversity of CalgaryToronto Rehabilitation InstituteSunnybrook Health Science Centre
Fundersnot available
KeywordsApathyNeurocognitivePsychologyDelphi methodClinical psychologyKappaScale (ratio)PsychiatryArtificial intelligenceCognitionComputer scienceCartographyMathematicsGeography

Abstract

fetched live from OpenAlex

Abstract Background Diagnostic criteria for apathy (DCA) in neurocognitive disorders were developed in 2021. As next steps, we assessed whether commonly‐used validated apathy scales map onto the DCA and identified which scales map best. Method Using a modified Delphi process, mapping of the Neuropsychiatric Inventory‐Clinician (NPI‐C) Apathy domain and the Apathy Evaluation Scale (AES) onto the DCA were assessed by surveying apathy experts. For each item on the scales, experts were asked to evaluate the degree of correspondence to the DCA globally, and the 3 DCA dimensions [diminished initiative, interest, and emotion]. Respondents voted “not at all” (scored as 0), “weakly” (scored as 1), or “strongly” (scored as 2). For each item, if the mean score was <0.5, the item was considered not mapped, for scores >1.5, the item was considered mapped, and items with scores 0.5‐1.5 were discussed further in a virtual consensus meeting. The surveys were then sent to other scientific community members, and the interrater reliability between the two groups was assessed using Cohen’s kappa. Result The surveys were completed by 12 experts. For NPI‐C apathy, 10/11 (90.9%) questions mapped primarily onto one dimension of the DCA: 2 onto “Initiative”, 6 onto “Interest”, and 2 onto “Emotion”. Of NPI‐C Apathy domain items, 9/11 (81.8%) mapped globally onto the DCA. For the AES, 7/18 (38.9%) questions mapped onto different dimensions of the DCA: 4 onto “Initiative”, 2 onto “Interest”, and 1 onto “Emotion”; 6/18 (33.3%) items mapped globally onto the DCA. The mean mapping scores of NPI‐C were significantly higher than those of the AES (t (27) = 2.25, p = 0.032) indicating better performance of NPI‐C in this exercise. These findings were confirmed in a consensus discussion. There was substantial agreement (kappa = 0.621) between the experts and the community group regarding mapping of NPI‐C but only moderate agreement (kappa = 0.553) regarding mapping of AES on the DCA. Conclusion More questions from the NPI‐C Apathy domain mapped strongly and uniquely both globally, and onto the 3 dimensions of the DCA, compared with the AES. Future research using the DCA, NPI‐C Apathy domain, and AES in clinical and research settings are needed to confirm these results.

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)0.022
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.056
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.065
GPT teacher head0.357
Teacher spread0.292 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2023
Admission routes1
Has abstractyes

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