MétaCan
Menu
← Back to cohort
Record W98243737 · doi:10.1093/pch/10.4.221

Case 1: The deadly danger of pertussis

2005· article· en· W98243737 on OpenAlexaff
Savithiri Ratnapalan, Patricia C. Parkin, Upton Allen

Bibliographic record

VenuePaediatrics & Child Health · 2005
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBacterial Infections and Vaccines
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineCracklesRespiratory rateTachypneaAnesthesiaChest radiographEmergency departmentPulse oximetryPhysical examinationHeart rateInternal medicineLungBlood pressureTachycardia

Abstract

fetched live from OpenAlex

A nine-week-old girl presented to the emergency department in the early fall with a one-day history of cough, tachypnea and poor feeding. Her rectal temperature was 37.9°C, pulse was 140 beats/min, respiratory rate was 40 breaths/min and oxygen saturation was 95% in room air. Her chest was clear, there were mild intercostal retractions, and the remainder of her examination was within normal limits. Blood cultures and nasopharyngeal (NP) swabs for pertussis and respiratory viruses were obtained. Her white blood cell count (WBC) was 15.3×109/L (53% polymorphs, 41% lymphocytes). A chest radiograph showed mild hyperinflation and perihilar bronchial thickening. The patient improved with salbutamol inhalations and was sent home on that medication. She returned two days later. Her temperature was 38.3°C, pulse was 180 beats/min, respiratory rate was 58 breaths/min to 76 breaths/min and oxygen saturation was 93% in room air. Chest examination revealed reduced air entry bilaterally with occasional crackles. Her WBC had risen to 22.5×109/L (57% polymorphs, 38% lymphocytes). She was admitted to hospital with the diagnosis of bronchiolitis.

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)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0090.006
Insufficient payload (model declined to judge)0.0050.002

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 head0.261
Teacher spread0.246 · 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 designCase report
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
Published2005
Admission routes1
Has abstractyes

Explore more

Same venuePaediatrics & Child Health→Same topicBacterial Infections and Vaccines→French-language works237,207→