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Record W2160167819 · doi:10.1177/1087054705283881

Symptoms Versus Impairment

2006· article· en· W2160167819 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

VenueJournal of Attention Disorders · 2006
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPsychologyFunctional impairmentClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Diagnosing ADHD based primarily on symptom reports assumes that the number/frequency of symptoms is tied closely to the impairment imposed on an individual's functioning. That presumed linkage encourages diagnosis more by Diagnostic and Statistical Manual of Mental Disorders (4th ed.) style symptom lists than well-defined, psychometrically sound assessments of impairment. The current study correlated measures reflecting each construct in four separate, large-scale ADHD research samples. Average correlation between symptoms and impairment accounted for less than 10% of variance. Symptoms never predicted more than 25% of the variance in impairment. When an ADHD group was formed according to a measure of current symptoms, the sample size shrunk by 77% when a criterion-based measure of impairment was added. The partial unlinking of symptoms and impairment has implications for decisions about the diagnostic process, research criteria for participant inclusion, prevalence estimates, gender ratios, evaluation of treatment effects, service delivery, and many other issues.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.049
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.015
GPT teacher head0.298
Teacher spread0.283 · 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