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Record W2282234988

One Year Treatment Costs of Trauma Care

2007· article· en· W2282234988 on OpenAlexaff
Sharada Weir, David S. Salkever, Frederick P. Rivara, Gregory J. Jurkovich, Avery B. Nathens, Ellen J. MacKenzie

Bibliographic record

VenueSSRN Electronic Journal · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineAcute careEmergency medicineTrauma centerRehabilitationHealth carePopulationMedical emergencyPhysical therapyRetrospective cohort studyEnvironmental healthSurgery
DOInot available

Abstract

fetched live from OpenAlex

Injuries have been estimated to account for approximately 10% of total U.S. medical expenditures. Data from the National Study on the Costs and Outcomes of Trauma (NSCOT) were used to develop a better understanding of the nature of these expenditures for a subgroup of moderately severe and severe injuries. Specifically, we present estimates of the treatment costs of trauma care in the acute setting (the index hospitalization for trauma) and for 12 months following injury. The NSCOT team recruited patients from 18 hospitals with a level 1 trauma center and 51 non-trauma center hospitals located in 14 states in several regions of the country. Patients 18 to 84 years old with a moderate-to-severe injury discharged between July 2001 and November 2002 were eligible. Complete data were obtained for 1104 patients who died in the hospital and 4087 patients who were discharged alive. Analyses employ data weighted to the population of eligible patients to adjust for the sampling protocol, whereby all deaths were included but live discharges were sampled, and to account for the fact that not all cases selected for inclusion were enrolled. Acute and post-acute costs of care were estimated from a combination of data sources: UB92 hospital bills (acute hospitalization), purpose-designed patient surveys conducted at 3- and 12-months post-injury (post-acute health services utilization across settings), and for the elderly sample, Medicare claims (both acute and post-acute utilization across settings). We have data on inpatient stays (acute, rehabilitation and skilled nursing facilities), outpatient physical and occupational therapy, other outpatient care (outpatient hospital care, PCP visits, etc.), home health, and informal care provided by family members. Inpatient charges were stepped down to costs by matching revenue center data from hospital bills with cost-to-charge ratios from the hospital's Medicare Cost Report at the cost department level. Inpatient costs were adjusted using regional wage and capital indexes. Where Medicare claims were available, outpatient costs were estimated as allowed charges. Unit cost estimates were computed from Medstat's MarketScan commercial database, Medicare Cost Reports, AHA data and other sources. All costs are expressed as constant 2005 dollars using appropriate price indices (e.g., Hospital PPI, Nursing Care Facility PPI, Professional Services Component of the CPI for Medical Care). We will present additional detail on our cost estimation methodology. Estimates of treatment costs will be presented by acute versus post-acute care, discharge disposition (hospital death vs. live discharge), age cohort, and type and severity of injury. Finally, we employ national estimates on incidence of traumatic injuries from AHRQ's Health Cost and Utilization Project (HCUP) database to arrive at an estimate of the annual cost of treating major trauma in the United States.

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)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.034
GPT teacher head0.273
Teacher spread0.239 · 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
Published2007
Admission routes1
Has abstractyes

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