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Record W7065223806

Economic Evaluation of the Pediatric Tele-resuscitation Intervention Pilot Study

2022· other· en· W7065223806 on OpenAlexaboutno aff

Bibliographic record

VenueBrock University Digital Repository (Brock University) · 2022
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic evaluationIntervention (counseling)TelemedicineEmergency departmentCost–benefit analysisQualitative researchGeneral partnershipHealth careCost effectiveness
DOInot available

Abstract

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Background: Pediatric Telemedicine Connecting Hospitals (Peds–TECH), a pediatric tele-resuscitation intervention, was developed in partnership with Niagara Health, Brock University and McMaster Children's Hospital. This network aims to enhance pediatric outcomes for infants and children who present to NH hospitals' emergency departments using telemedicine (TM). Objectives: This study aimed to (i) identify out-of-pocket (OOP) costs and time/productivity losses for parents of pediatric patients seeking care at the emergency department (ED); (ii) calculate financial spending within the Peds-TECH intervention and estimate health resource utilization (HRU) costs; (iii) determine the clinical effectiveness of the Peds-TECH intervention on emergency transfers to a tertiary care hospital, and survival/mortality outcomes; and (iv) estimate an incremental cost effectiveness ratio (ICER) of Peds-TECH intervention compared to usual care (i.e., conventional means of providing resuscitation in ED). Methods: A mixed methods quasi-experimental research design was used for this project that included quantitative and qualitative methods in the economic evaluation. This study used qualitative research in the form of interviews, to explore OOP expenses incurred by parents of pediatric patients treated with the Peds-TECH intervention, and quantitative research when determining costs related to health technology and HRU. The ICER represents the incremental costs (Canadian dollar) relative to the years of life lost (YLL) averted in the intervention compared to usual care. The willingness-to-pay (WTP) threshold of $50,000 was considered to interpret the cost-effectiveness of the Peds-TECH intervention. Results: Qualitative interviews with parents revealed OOP expenses pertinent to personal transportation, food/meal, and time/productivity losses. In terms of OOP costs, the largest category in terms of both overall cost ($435) and response rate (5/5) was travel/parking costs. The next most common response to expenses incurred was the cost of food/meals ($80) with a response rate of 3/5. One participant did report paying for hotel accommodations for one day ($120). The odds ratio (OR) for mortality among cases was 0.498 (95% CI, 0.173, 1.43) compared to controls (i.e., usual care), indicating that the intervention was protective against mortality. An ICER of $64.61 per YLL averted was determined from the CEA, which represents the cost for an additional life-year saved in the intervention group compared to the control. In the deterministic sensitivity analysis, the ICER ranged as low as (- 138.72) and as high as (489.75) per YLL averted. The probabilistic analysis indicated 96.1% probability that Peds-TECH is cost-effective under a $5K WTP threshold, which is much lower than the standard $50K WTP threshold. Conclusion: This study acknowledged gaps surrounding the OOP costs associated with pediatric emergencies in the Niagara Region. The Peds-TECH intervention is considered clinically effective in preventing mortality among pediatric patients compared to usual care. As well, it was determined that the Peds-TECH intervention is highly cost-effective with an ICER of $64.61 per YLL averted. Further research is warranted to assess the long-term costs and health outcomes of pediatric patients undergoing the Peds-TECH intervention.

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)0.037
metaresearch head score (Gemma)0.058
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.037
Threshold uncertainty score0.195

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.058
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.016
GPT teacher head0.225
Teacher spread0.209 · 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 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
Published2022
Admission routes1
Has abstractyes

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