Evidence map›Paper›PMID 31905455›Full record

ArticleNeurospine2019

Predictive Analytics in Spine Oncology Research: First Steps, Limitations, and Future Directions.

Elie Massaad, Nida Fatima, Muhamed Hadzipasic, Christopher Alvarez-Breckenridge, Ganesh M Shankar, John H Shin

Abstract read
In one paragraph

Article in Neurospine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Current and Emerging Approaches for Spine Tumor Treatment.International journal of molecular sciences · 2022
    Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. 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

6 authors.

Elie MassaadDepartment of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Nida FatimaDepartment of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Muhamed HadzipasicDepartment of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Christopher Alvarez-BreckenridgeDepartment of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Ganesh M ShankarDepartment of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
John H ShinDepartment of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The potential of big data analytics to improve the quality of care for patients with spine tumors is significant. At this moment, the application of big data analytics to oncology and spine surgery is at a nascent stage. As such, efforts are underway to advance data-driven oncologic care, improve patient outcomes, and guide clinical decision making. This is both relevant and critical in the practice of spine oncology as clinical decision making is often made in isolation looking at select variables deemed relevant by the physician. With rapidly evolving therapeutics in surgery, radiation, interventional radiology, and oncology, there is a need to better develop decision-making algorithms utilizing the vast data available for each patient. The challenges and limitations inherent to big data analyses are presented with an eye towards future directions.

Indexed as

Artificial intelligenceMachine learningPredictive analyticsPrimary spine tumorSpine metastasesSpine tumor

Identifiers

PMID31905455
PMCPMC6944986

What OpenQuestion holds

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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.