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Record W2083955277 · doi:10.1016/j.jalz.2011.05.017

IC‐03‐05: Estimating the impact of differences among protocols for manual hippocampal segmentation on Alzheimer's disease‐related atrophy: Preparatory phase for a harmonized protocol

2011· article· en· W2083955277 on OpenAlexaff
Marina Boccardi, Martina Bocchetta, Rossana Ganzola, Nicolas Robitaille, Alberto Redolfi, George Bartzokis, Richard Camicioli, John G. Csernansky, Mony J. de Leon, Leyla deToledo‐Morrell, Ronald Killiany, Stéphane Lehéricy, Johannes Pantel, Jens C. Pruessner, Hilkka Soininen, Craig Watson, Simon Duchesne, Clifford R. Jack, Giovanni B. Frisoni

Bibliographic record

VenueAlzheimer s & Dementia · 2011
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill UniversityUniversity of AlbertaUniversité Laval
Fundersnot available
KeywordsAtrophyNeuroimagingMagnetic resonance imagingSample size determinationPsychologyHippocampal formationBrain sizeSubiculumSegmentationCerebrospinal fluidInternal medicineNeuroscienceMedicineRadiologyStatisticsArtificial intelligenceComputer science

Abstract

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To quantify the impact of the differences among Magnetic Resonance Imaging (MRI)-based hippocampal segmentation protocols on volume estimates of Alzheimer's disease (AD)-related atrophy, in order to support evidence-based decisions for an internationally harmonized protocol. A harmonized procedure is required, since quantitative MRI should help diagnosis and tracking of AD. A survey of segmentation protocols allowed to operationalize the landmarks variability into segmentation units (SUs) (Figure), and their impact on volume estimates has been preliminarily quantified. A power analysis was carried out on a preliminary sample, to define the sample size allowing reliable computation. Then, we manually traced each SU within the right and left hippocampi of a larger sample of Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, which included Mild Cognitive Impairment (MCI) patients who subsequently converted to AD and AD patients, all with abnormal Cerebrospinal Fluid (CSF) Aß levels, and controls (CTRL), with normal CSF Aß levels. The power analysis indicated a required sample size for the quantification of SUs impact on AD-related volume differences of n=77 (31 CTRL, 23 MCI, 23 AD). So far, 40 subjects (16 CTRL, 12 MCI, 12 AD) have been traced and analyzed. The minimum hippocampal body (red SU in Figure) accounted for over 62% of AD-related volume difference across groups (left: 68.5%, right: 62%, p<0.001); the left alveus/fimbria (yellow SU in Figure) for 7.5% (p=0.01) and the right alveus/fimbria for 3% (p=0.7); the subiculum (green SUs in Figure) for 5% bilaterally (left: p=0.08; right: p=0.03); the left tail (blue SUs in Figure) for 19% (p=0.003), and the right tail for the 30% (p=0.001) of the global difference across groups. 3D rendering of segmentation units (SUs) as traced out from the right hippocampus of a normal subject. SUs represent the differences in tracing criteria among protocols.7 Red = minimum hippocampal body, common to all protocols; Yellow = alveus/fimbria; Green = different criteria to trace the medial border at the level of the subiculum; Blue = different criteria to trace the most caudal portion (tail).

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)0.160
metaresearch head score (Gemma)0.206
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.160
Threshold uncertainty score0.844

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1600.206
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0020.003
Science and technology studies0.0020.003
Scholarly communication0.0030.002
Open science0.0050.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.003

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 head0.089
GPT teacher head0.423
Teacher spread0.333 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations4
Published2011
Admission routes1
Has abstractyes

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