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Record W2064850416 · doi:10.3138/ptc.2010-10

Physical Therapy Management of Ventilated Patients with Acute Respiratory Distress Syndrome or Severe Acute Lung Injury

2010· article· en· W2064850416 on OpenAlexaffvenue
Frank Chung, Dan Mueller

Bibliographic record

VenuePhysiotherapy Canada · 2010
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsBurnaby Hospital
Fundersnot available
KeywordsMedicineARDSProne positionPsychological interventionIntensive care medicineAcute respiratory distressPhysical therapySittingDistressEmergency medicineLungSurgeryNursingInternal medicine

Abstract

fetched live from OpenAlex

The early use of prone positioning, longer duration in prone lying (i.e., sufficient dosage), and use of prone positioning over a sufficient number of days are important components of the prone-positioning protocol for ventilated patients with ARDS/ALI.47–49,70,72,73 Kinetic therapy or lateral positioning with head of bed >30° and sitting with head of the bed >30° may be used for routine positioning of patients with ventilated ARDS/ALI.28,42,43 Step-wise early mobilization of ICU patients is safe and is associated with favourable outcomes in terms of both hospital length of stay and functional ability of the patient.29,30,32 Early intervention of sufficient frequency and duration and over an adequate period are the key to success for many physiotherapy interventions for ventilated ARDS/ALI patients. This review provides a starting point for physiotherapy guidelines in the management of patients with ARDS/ALI, but more clinical research is needed, and, of course, the patient's best interest is paramount when research findings are incorporated into clinical practice.88,89 Even given the complexity involved in clinical research on ICU care of severely ill patients, there may be some simple but important treatment interventions for physical therapists to use that may save lives when properly administered.90

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.006
GPT teacher head0.265
Teacher spread0.259 · 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 designBench or experimental
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

Citations8
Published2010
Admission routes2
Has abstractyes

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