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Conventional SPECT Versus 3D Thresholded SPECT Imaging in the Diagnosis of ADHD: A Retrospective Study

2014· article· en· W2130226442 on OpenAlexaff
Howard Schneider, Marc Freeman, Mary Kay McLean, Muriel J. van Lierop, Jonathan Schneider

Bibliographic record

VenueJournal of Neuropsychiatry · 2014
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsSingle-photon emission computed tomographyNuclear medicineSpect imagingEmission computed tomographyConfidence intervalMedicineRetrospective cohort studyCerebral blood flowRadiologyPositron emission tomographyInternal medicine

Abstract

fetched live from OpenAlex

Brain single photon emission CT (SPECT) scans indirectly show functional activity via measurement of regional cerebral blood flow. In conventional SPECT scans, the typical tomographic slices are produced. In three-dimensional thresholded SPECT scans, pixels representing activity below a certain threshold are discarded. A retrospective analysis of 427 patients shows that three-dimensional thresholded SPECT scans yield a sensitivity for predicting clinical attention deficit hyperactivity disorder of 54% [95% confidence interval (CI), 46%-61%; specificity, 76%; 95% CI, 71%-81%] compared with 4% sensitivity [95% CI, 2%-8%; specificity, 97%; 95% CI, 94%-98%] for conventional SPECT scans. For 170 of the patients originating from a general psychiatry practice, conventional SPECT showed 10% sensitivity (95% CI, 4%-23%) and 98% specificity (95% CI, 93%-99%), whereas three-dimensional thresholded SPECT showed 83% sensitivity (95% CI, 68%-91%) and 77% specificity (95% CI, 69%-83%). These findings indicate that a much stronger signal is obtained when the three-dimensional thresholded SPECT scan is performed rather than the conventional SPECT scan in detecting attention deficit hyperactivity disorder and suggest similar results may be obtained for other psychiatric disorders.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.012
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.050
GPT teacher head0.347
Teacher spread0.297 · 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.

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

Citations9
Published2014
Admission routes1
Has abstractyes

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