Evidence map›Paper›PMID 34779726›Full record

ArticleThe journal of spinal cord medicine2021

Identifying cases of spinal cord injury or disease in a primary care electronic medical record database.

John Shepherd, Karen Tu, Jacqueline Young, Jawad Chishtie, B Catharine Craven, Rahim Moineddin, Susan Jaglal

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The journal of spinal cord medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. ShuffleLeNet architecture for spinal cord injury classification and level detection.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Animal models of compression spinal cord injury.Journal of neuroscience research · 2022
    Review
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

John ShepherdRehabilitation Sciences Institute, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0001-8072-3775
Karen TuDepartment of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0003-0883-4934
Jacqueline YoungInstitute for Clinical Evaluative Sciences, Toronto, Ontario, Canada.ORCID 0000-0003-0994-1934
Jawad ChishtieRehabilitation Sciences Institute, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0001-8650-4469
B Catharine CravenInstitute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0001-8234-6803
Rahim MoineddinDepartment of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0002-5506-084X
Susan JaglalInstitute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0002-2930-1443

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo identify cases of spinal cord injury or disease (SCI/D) in an Ontario database of primary care electronic medical records (EMR).

designA reference standard of cases of chronic SCI/D was established via manual review of EMRs; this reference standard was used to evaluate potential case identification algorithms for use in the same database.

settingElectronic Medical Records Primary Care (EMRPC) Database, Ontario, Canada.

participantsA sample of 48,000 adult patients was randomly selected from 213,887 eligible patients in the EMRPC database.

interventionsN/A. MAIN OUTCOME MEASURE(S): Candidate algorithms were evaluated using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F-score.

results126 cases of chronic SCI/D were identified, forming the reference standard. Of these, 57 were cases of traumatic spinal cord injury (TSCI), and 67 were cases of non-traumatic spinal cord injury (NTSCI). The optimal case identification algorithm used free-text keyword searches and a physician billing code, and had 70.6% sensitivity (61.9-78.4), 98.5% specificity (97.3-99.3), 89.9% PPV (82.2-95.0), 94.7% NPV (92.8-96.3), and an F-score of 79.1.

conclusionsIdentifying cases of chronic SCI/D from a database of primary care EMRs using free-text entries is feasible, relying on a comprehensive case definition. Identifying a cohort of patients with SCI/D will allow for future study of the epidemiology and health service utilization of these patients.

Indexed as

Electronic Health RecordsSpinal Cord InjuriesAdultDatabases, FactualHumansOntarioPrimary Health CareCase identificationElectronic medical recordsPrimary health careSpinal cord injury

Identifiers

PMID34779726
PMCPMC8604482

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.