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Record W1011638209 · doi:10.1155/2001/818305

Appearances Can Be Deceiving: What Is the Diagnosis for this Community‐Acquired Pneumonia?

2001· article· en· W1011638209 on OpenAlexaffabout
Frank YH Lin, Coleman Rotstein

Bibliographic record

VenueCanadian Journal of Infectious Diseases and Medical Microbiology · 2001
Typearticle
Languageen
FieldMedicine
TopicPleural and Pulmonary Diseases
Canadian institutionsMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsMedicineMalaiseVomitingNauseaProductive CoughPneumoniaSputumPresentation (obstetrics)Emergency departmentChest painSputum cultureCommunity-acquired pneumoniaSurgeryLungTuberculosisInternal medicinePathology

Abstract

fetched live from OpenAlex

A and asbestos exposure (in the 1970s) presented to the emergency department with a one‐month history of progressive dyspnea, right‐sided pleuritic chest pain, cough productive of white‐coloured sputum and malaise. His health problems had commenced four months before presentation while he was vacationing at a northern Ontario resort. At that time, he had felt unwell and had developed a fever with rightsided pleuritic chest pain that radiated to his right shoulder. The diagnosis was an upper respiratory tract infection, made by the local physician; the patient was treated with a 10‐day course of cephalexin. Although his condition had initially improved after the antibiotic therapy, during the month before presentation he had experienced increasing fatigue, cough with clear sputum production and a loss of appetite. He also developed worsening right‐sided pleuritic chest pain that radiated to the right shoulder, dyspnea and orthopnea. He had no nausea, vomiting, diarrhea or hemoptysis. However, he had lost 4 kg and had drenching night sweats over the previous three and a half months. Further history revealed that he had drunk well water during his vacation in northern Ontario and that several families who were with him at that time also became ill, although he was not aware of the nature of their symptoms.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.260
Teacher spread0.242 · 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

Citations0
Published2001
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Infectious Diseases and Medical MicrobiologySame topicPleural and Pulmonary DiseasesFrench-language works237,207