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Assessment of Partial Sequencing of the 65-Kilodalton Heat Shock Protein Gene ( <i>hsp65</i> ) for Routine Identification of <i>Mycobacterium</i> Species Isolated from Clinical Sources

2004· article· en· W2126556456 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 Clinical Microbiology · 2004
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsUniversity of British ColumbiaBC Centre for Disease Control
Fundersnot available
KeywordsBiology16S ribosomal RNAIdentification (biology)Sequence analysisDNA sequencingMycobacteriumMicrobiologyRibosomal RNAGeneGeneticsBacteriaEcology

Abstract

fetched live from OpenAlex

We assessed the ability of an in-house database, consisting of 111 hsp65 sequences from putative and valid Mycobacterium species or described groups, to identify 689 mycobacterial clinical isolates from 35 species or groups. A preliminary assessment indicated that hsp65 sequencing confirmed the identification of 79.4% of the isolates from the 32 species examined, including all Mycobacterium tuberculosis complex isolates, all isolates from 13 other species, and 95.6% of all M. avium-M. intracellulare complex isolates. Identification discrepancies were most frequently encountered with isolates submitted as M. chelonae, M. fortuitum, M. gordonae, M. scrofulaceum, and M. terrae. Reexamination of isolates with discrepant identifications confirmed that hsp65 identifications were correct in a further 40 isolates. This brought the overall agreement between hsp65 sequencing and the other identification methods to 85.2%. The remaining 102 isolates had sequence matches below our acceptance criterion, had nondifferential sequence matches between two or more species, were identified by 16S rRNA sequencing as a putative taxonomic group not contained in our database, or were identified by hsp65 and 16S rRNA gene sequencing as a species not in our biochemical test database or had conflicting identifications. Therefore, to incorporate the unconfirmed isolates it was necessary to create 29 additional entries in our hsp65 identification database: 18 associated with valid species, 7 indicating unique sequences not associated with valid or putative species or groups, and 4 associated with unique, but currently described taxonomic groups. Confidence in the hsp65 sequence identification of a clinical isolate is best when sequence matches of 100% occur, but our data indicate that correct identifications can be confidently made when unambiguous matches exceeding 97% occur, but are dependent on the completeness of the database. Our study indicates that for hsp65 sequencing to be an effective means for identifying mycobacteria a comprehensive database must be constructed. hsp65 sequencing has the advantage of being more rapid and less expensive than biochemical test panels, uses a single set of reagents to identify both rapid- and slow-growing mycobacteria, and can provide a more definitive identification.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.065
GPT teacher head0.389
Teacher spread0.325 · 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