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Record W2140706205 · doi:10.1503/cmaj.150085

Effect of older age on treatment decisions and outcomes among patients with traumatic spinal cord injury

2015· article· en· W2140706205 on OpenAlexafffundvenue
Henry Ahn, Christopher S. Bailey, Carly S. Rivers, Vanessa K. Noonan, Eve C. Tsai, Daryl R. Fourney, Najmedden Attabib, Brian K. Kwon, Sean Christie, Michael G. Fehlings, Joel Finkelstein, R. John Hurlbert, Andrea Townson, Stefan Parent, Brian Drew, Jason Chen, Marcel F. Dvorak

Bibliographic record

VenueCanadian Medical Association Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of CalgaryOttawa HospitalUniversity of British ColumbiaMcMaster UniversitySt. Paul's HospitalDalhousie UniversityUniversity of TorontoUniversité de MontréalUniversity of SaskatchewanPraxis Spinal Cord InstituteWestern University
FundersAOSpineWestern Economic Diversification CanadaDePuy Synthes SpineHealth CanadaRick Hansen InstituteBioClinica
KeywordsMedicineSpinal cord injuryRehabilitationCohortInjury Severity ScoreAcute careAbbreviated Injury ScaleRetrospective cohort studyCohort studyPoison controlInjury preventionPhysical therapySpinal cordInternal medicineEmergency medicineHealth care

Abstract

fetched live from OpenAlex

BACKGROUND: Older people are at increased risk of traumatic spinal cord injury from falls. We evaluated the impact of older age (≥ 70 yr) on treatment decisions and outcomes. METHODS: We identified patients with traumatic spinal cord injury for whom consent and detailed data were available from among patients recruited (2004-2013) at any of the 31 acute care and rehabilitation hospitals participating in the Rick Hansen Spinal Cord Injury Registry. Patients were assessed by age group (< 70 v. ≥ 70 yr). The primary outcome was the rate of acute surgical treatment. We used bivariate and multivariate regression models to assess patient and injury-related factors associated with receiving surgical treatment and with the timing of surgery after arrival to a participating centre. RESULTS: Of the 1440 patients included in our study cohort, 167 (11.6%) were 70 years or older at the time of injury. Older patients were more likely than younger patients to be injured by falling (83.1% v. 37.4%; p < 0.001), to have a cervical injury (78.0% v. 61.6%; p = 0.001), to have less severe injuries on admission (American Spinal Injury Association Impairment Scale grade C or D: 70.5% v. 46.9%; p < 0.001), to have a longer stay in an acute care hospital (median 35 v. 28 d; p < 0.005) and to have a higher in-hospital mortality (4.2% v. 0.6%; p < 0.001). Multivariate analysis did not show that age of 70 years or more at injury was associated with a decreased likelihood of surgical treatment (adjusted odds ratio [OR] 0.48, 95% confidence interval [CI] 0.22-1.07). An unplanned sensitivity analysis with different age thresholds showed that a threshold of 65 years was associated with a decreased chance of surgical treatment (OR 0.39, 95% CI 0.19-0.80). Older patients who underwent surgical treatment had a significantly longer wait time from admission to surgery than younger patients (37 v. 19 h; p < 0.001). INTERPRETATION: We found chronological age to be a factor influencing treatment decisions but not at the 70-year age threshold that we had hypothesized. Older patients waited longer for surgery and had a substantially higher in-hospital mortality despite having less severe injuries than younger patients. Further research into the link between treatment delays and outcomes among older patients could inform surgical guideline development.

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)0.011
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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.030
GPT teacher head0.365
Teacher spread0.336 · 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

Citations70
Published2015
Admission routes3
Has abstractyes

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