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Record W2003769714 · doi:10.1159/000193859

Indications for Pulmonary Function Testing

2009· article· en· W2003769714 on OpenAlexaff
V. Lopéz-Majano, G Renzi

Bibliographic record

VenueRespiration · 2009
Typearticle
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsHôpital Notre-Dame
Fundersnot available
KeywordsMedicineSpirometryPulmonary function testingDiffusing capacityPulmonary Diffusing CapacityLung volumesContraindicationLungVentilation (architecture)Functional residual capacitySurgeryIntensive care medicineCardiologyAnesthesiaInternal medicineLung functionPathologyAsthma

Abstract

fetched live from OpenAlex

Pulmonary function testing is needed to determine the pathophysiology present in the patient with cardiopulmonary disease. Blood gases and pH should be obtained during emergency situations and during cranial, thoracic, and extensive cervical or abdominal surgery. Lung function tests can be divided in global such as spirometry and diffusing capacity which study the ventilation and transfer of gases and regional determinations of ventilation and perfusion. Both types of tests complement each other and should be used together. The spirometry should consist at least of determination of the vital capacity and is determined in the first second to ascertain if there is obstructive lung disease. Some tests such as flow-volume curves, alveolar-arterial gradients and closing volume are very useful to detect early pulmonary disease before any symptoms or findings are present. This is probably one of the most important medical indications for pulmonary function testing. Before certain types of surgery pulmonary function testing is indicated; if the spirometry and diffusing capacity tests are normal, there is no pulmonary contraindication for the planned surgery. In chest surgery if there is significant compromise of the spirometry and diffusing capacity regional lung function tests are indicated to study the pathophysiology at regional level, thus trying to circumscribe the lung resection to the diseased areas.

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)0.000
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 score0.934
Threshold uncertainty score0.191

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.082
GPT teacher head0.340
Teacher spread0.258 · 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

Citations3
Published2009
Admission routes1
Has abstractyes

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