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Record W6907979431 · doi:10.25384/sage.c.6327973.v1

Use of Palliative Oxygen in Cancer Patients

2022· other· en· W6907979431 on OpenAlexaboutno aff

Bibliographic record

VenueSage Journals Data · 2022
Typeother
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedical prescriptionPalliative careHypoxemiaCancerOxygen therapyReimbursementOxygen saturation

Abstract

fetched live from OpenAlex

Background: Despite the lack of evidence to support the use of palliative oxygen to relieve dyspnea at the end of life, its prescription is widespread and often supported by local and national practice guidelines. Objectives: The objectives of this study were (1) to determine to what extent oxygen prescriptions meet the proposed prescription criteria in our institution, (2) to examine the indication of individual prescriptions in relation to the severity of dyspnea and (3) to review the utilization of opioids in patients receiving palliative oxygen. Methods: Retrospective chart review of cancer patients who were prescribed palliative oxygen between April 2015 and January 2020 through a respiratory home care program in Quebec City, Canada. According to provincial prescription guidelines, palliative oxygen was provided and reimbursed in case of severe hypoxemia (pulse oximetry saturation at rest < 88%) in cancer patients with an estimated prognosis of less than 3 months. Results: 134 patients receiving palliative oxygen were included; 25 (19%) did not fulfill reimbursement criteria. Median survival was 44 days. At initiation of palliative oxygen, 48 patients (36%) had only mild or moderate dyspnea (Medical Research Council dyspnea score 1-3), 26 (19%) did not receive opioids, and 9 (7%) were prescribed palliative oxygen without being dyspneic or receiving opioids. Conclusion: Most prescriptions of palliative oxygen met the proposed prescription criteria in our institution. Half of those who received palliative oxygen were only mildly dyspneic and/or were not receiving opioids at the time of the prescription.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.060
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.380
GPT teacher head0.455
Teacher spread0.075 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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